{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bikeshare数据集上的数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# _*_ coding:utf-8 _*_\n",
    "import sys"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data manipulation\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plotting\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#读入数据\n",
    "df = pd.read_csv(\"day.csv\")\n",
    "\n",
    "#观察前5行\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "#数据基本信息\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据基本信息\n",
    "\n",
    "一、样本数N：731，没有缺失值\n",
    "\n",
    "二、特征维数：15，特征说明如下：\n",
    "1.Instant：记录号 \n",
    "2.Dteday：日期 \n",
    "3.Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "4.yr：年份，(0: 2011, 1:2012) \n",
    "5.mnth：月份( 1 to 12) \n",
    "6.holiday：是否是节假日 \n",
    "7.weekday：星期中的哪天，取值为0～6 \n",
    "8.workingday：是否工作日 1=工作日 （是否为工作日，1为工作日，0为周末或节假日 \n",
    "9.weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾） \n",
    "10.temp：气温摄氏度 \n",
    "11.atemp：体感温度 \n",
    "12.hum：湿度 \n",
    "13.windspeed：风速 \n",
    "14.casual：非注册用户个数 \n",
    "15.registered：注册用户个数 \n",
    "\n",
    "三、标签y cnt：给定日期（天）总租车人数，响应变量y （cnt = casual + registered）\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(731, 16)\n"
     ]
    }
   ],
   "source": [
    "#样本数目和特征维数\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['instant', 'dteday', 'season', 'yr', 'mnth', 'holiday', 'weekday',\n",
      "       'workingday', 'weathersit', 'temp', 'atemp', 'hum', 'windspeed',\n",
      "       'casual', 'registered', 'cnt'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "#列的名称\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#数值型特征的描述统计量\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1数值型特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xd69b1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#目标cnt（给定日期总租车人数）的直方图\n",
    "fig = plt.figure()\n",
    "sns.distplot(df[\"cnt\"], bins = 30, kde = True)\n",
    "plt.xlabel(\"TotalCount\", fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000000000C3BC780>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000C42FCF8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000000000D432CF8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000000000D46DD68>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf7b780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#数值特征直方图\n",
    "numerical_features = [\"temp\", \"atemp\", \"hum\", \"windspeed\"]\n",
    "df[numerical_features].hist(bins = 30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.离散特征的统计分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    188\n",
       "2    184\n",
       "1    181\n",
       "4    178\n",
       "Name: season, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#观察类别型特征\n",
    "df[\"season\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    366\n",
       "0    365\n",
       "Name: yr, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"yr\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12    62\n",
       "10    62\n",
       "8     62\n",
       "7     62\n",
       "5     62\n",
       "3     62\n",
       "1     62\n",
       "11    60\n",
       "9     60\n",
       "6     60\n",
       "4     60\n",
       "2     57\n",
       "Name: mnth, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"mnth\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    710\n",
       "1     21\n",
       "Name: holiday, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"holiday\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    105\n",
       "1    105\n",
       "0    105\n",
       "5    104\n",
       "4    104\n",
       "3    104\n",
       "2    104\n",
       "Name: weekday, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"weekday\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    500\n",
       "0    231\n",
       "Name: workingday, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"workingday\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    463\n",
       "2    247\n",
       "3     21\n",
       "Name: weathersit, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"weathersit\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.特征与目标之间的关系\n",
    "\n",
    "### 3.1每年骑行量的分布\n",
    "violinplot中用x表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xde0c5f8>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdf5ee10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data = df[[\"yr\", \"cnt\"]], x = \"yr\", y = \"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出2011和2012两年骑行量分布差异还是挺大的"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2一年中每天的骑行量\n",
    "用颜色参数hue表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'dayly distribution of counts')]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEWCAYAAABBvWFzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvNQv5yAAAIABJREFUeJzsnXd4VFXegN8zMykQQi/Si3QUEBVBRVFRsCB2RVkb6q6uq7vqrmtbXT97we7asIAK2MUGooiI9N6k19AhBEhCkinn++PMMPfeuW0mkxDgvs/DQ+aWM2fa+Z1fF1JKPDw8PDw8yovvYE/Aw8PDw+PwwBMoHh4eHh5pwRMoHh4eHh5pwRMoHh4eHh5pwRMoHh4eHh5pwRMoHh4eHh5pwRMoHikjhHhfCPFYVRknOpYUQrSN/v2GEOKhNI3bQghRKITwRx9PEkLclI6xo+P9IIS4Ll3jJfG8jwkhdgohtlb2c3scfgQO9gQ8PCoKKeVf3FwnhFgH3CSl/MlmrA1AjXTMSwjxCNBWSjlEM/656Rg7yXk0B+4GWkopt1f285shhOgLfCilbHaw5+KRPJ6G4uHhgBDicN14tQR2VRVh4nHo4wkUD9cIIY4TQswVQuwTQowBsjXn6gghvhVC7BBC7I7+3Sx67nIhxBzDWHcLIb4yeY7FQoiBmscZUZNMd4s5/VMIsUUIsVkIcaPh3AFTmhCifnROBUKIfCHEb0IInxBiJNAC+CZq0vqXEKJV1HQ2VAixAZioOaYVLkcLIWYKIfYIIb4WQtSNPldfIUSeYS7rhBD9hBADgPuBK6PPtyB6/oAJLTqvB4UQ64UQ24UQI4QQtaLnYvO4TgixIfrePGDzmdWK3r8jOt6D0fH7AROAJtF5vG9x/yAhxHwhxF4hxOro/BFCNBFCjI2+l6uEEDebve9m70f0vbhHCLEw+t6NEUJkCyFygB80cyqMPk9PIcTs6By2CSGGWb1ej4OLJ1A8XCGEyAS+AkYCdYFPgUs1l/iA91C73hbAfuDV6LmxQGshRCfN9UOiYxkZET0X4zxgi5RyvsmcBgD3AGcD7YB+Ni/hbiAPaAA0Qi3qUkr5J2ADMFBKWUNK+YzmntOBTkB/izGvBW4EmgAh4GWb5wf1hOOAJ4Ax0efrZnLZ9dF/ZwBtUKa2Vw3XnAp0AM4C/mN4b7W8AtSKjnN6dM43RM175wKbo/O43nijEKIn6vP4J1AbOA1YFz09CvV+NgEuA54QQpxl/+p1XAEMAFoDXYHrpZRFhjnVkFJuBl4CXpJS1gSOBj5J4nk8KhFPoHi4pReQAbwopQxKKT8DZsVOSil3SSk/l1IWSyn3AY+jFjCklKXAGKKCQgjRBWgFfGvyPB8C5wkhakYf/wlzwQNqUXpPSrk4uhg9YjP/INAY5S8ISil/k86F7B6RUhZJKfdbnB+pee6HgCtE1GlfTq4Bhkkp10gpC4H7gKsM2tF/pZT7pZQLgAVAgmCKzuVK4D4p5T4p5TrgedR76oahwLtSyglSyoiUcpOUclnU93IqcK+UsiQq7N9JYlyAl6WUm6WU+cA3gKkGGiUItBVC1JdSFkoppyfxPB6ViCdQPNzSBNhkWITXx/4QQlQXQrwZNavsBSYDtTUL7AfA1UIIgVp4PokKGh3RHenvwKVCiNqoHetHNnPaaDYfE54FVgE/CiHWCCH+bfdio2xM4vx6lMCt72JcJ5qgfy3rUQE0jTTHtFFZxZgHDNQHMk3GaupyHs2B1Rbzy49uHFIZF9zNP8ZQoD2wTAgxSwhxQRLP41GJeALFwy1bgKZRgRCjhebvu1EmmJOiponToscFQHRXWQb0Aa7GWusAJXyGAJcD06SUm2zm1NxiPjqiO/S7pZRtgIHAXRoTjZWm4qTBGJ87COwEioDqsRNRodogiXE3o0yH2rFDwDaH+4zsjM7JOJbV+2lkI8rEZDa/ukKIXItxda8fOMrl84HJeyOlXCmlHAw0BJ4GPov6WzyqGJ5A8XDLNNSidocQIiCEuAToqTmfi/KbFESd0w+bjDEC5QsISSmn2DzXV0AP4M7oPVZ8AlwvhOgshKhu8ZwACCEuEEK0jQrEvUA4+g/UQt3G5nmsGKJ57keBz6SUYWAFkC2EOF8IkQE8CGRp7tsGtBJCWP3+RgH/EEK0FkLUIO5zCSUzuehcPgEeF0LkCiFaAnehzIpuGA7cIIQ4K+rIbyqE6Cil3AhMBZ6MOtO7orSImCY5H2W2rCuEOAr4exLT3gbUiwUhAAghhgghGkgpI0BB9HDY9G6Pg4onUDxcIaUsAy5BOYt3o2zzX2gueRGohtoVTwfGmQwzEjgGe+2EqM/ic5TD9gub636IPu9ElDlros2w7YCfgEKUcHxdSjkpeu5J4EGhIsDusZubgZHA+yjzTTZwR3Ree4DbUH6FTagduzbq69Po/7uEEHNNxn03OvZkYC1QAvwtiXlp+Vv0+dcAU4CPo+M7IqWcCdwAvADsAX4lru0MRvnBNgNfAg9LKSdEz41E+XXWAT+i/GeukFIuQwnUNdHPownKeb9ECFGIctBfJaUscTumR+UhvAZbHpWFEKIasB3oIaVc6XDtf4D22uQ/Dw+Pqs3hmrDlUTW5FZjlQpjURZlQkoka8vDwOMh4AsWjUhCqvIkALnK47maUGWuklHJyJUzNw8MjTXgmLw8PDw+PtOA55T08PDw80sJhafKqX7++bNWq1cGehoeHh8chxZw5c3ZKKRs4X2nOYSlQWrVqxezZsw/2NDw8PDwOKYQQdtUmHPFMXh4eHh4eacETKB4eHh4eacETKB4eHh4eaeGw9KF4eHh4HCyCwSB5eXmUlFTd6jDZ2dk0a9aMjIyMtI7rCRQPDw+PNJKXl0dubi6tWrVCX5y7aiClZNeuXeTl5dG6deu0ju2ZvDw8PDzSSElJCfXq1auSwgRACEG9evUqRIPyBIqHh4dHmqmqwiRGRc3PM3l5eHgcHDbMgBlvwOa5kFkDOl0IPW+G6nUP9sw8UsQTKB4eHpXPnPfhm7+ja9C4bTEsGAU3fA81mxysmXmUA8/k5eHhUbns2QTf3Y1pJ+Tda2H8/ZU+pUOJcLjqNqv0NBQPD4/KZeFoiNh0M/7jGyjONzd9SQkbZ0L+ashpCG1OB396Q18PNg899BD169fnzjvvBOCBBx6gUaNGfPnllzRu3Jj58+ezdOnSgzxLczyB4uHhUbkUbLA/HwnBvq2JAmXbEvj8JtiuWUxzG8PAl6B9//TP8yAxdOhQLrnkEu68804ikQijR4/mmWeeYebMmSxevDjtob7pxBMoHh4elUvNZvbnhR9yj9IfK9wOIwZB0Q798X1bYPQ1cON4aHZ8eud5kGjVqhX16tVj3rx5bNu2jeOOO4569erRs2fPKi1MwPOheHh4VDbdrlJCw4qO5yVqJ7PfTRQmMSJBmDIsffOrAtx00028//77vPfee9x4440A5OTkHORZOeMJlCOZcEiZH4p2HeyZeBxJ1G4O5z5tfq5Wczj93/Drs/BGH3jlePjiFuVXsWP1L+mf50Hk4osvZty4ccyaNYv+/Q8dc55n8joSiYRhygsw400o2q6OtTwF+j0CzXsezJl5HCn0vBkadIAPBsaPHXs5nHwnjLkGdq+LH9+1ysWAh1cr88zMTM444wxq166N32+jzVUxPIFyJDL2Dpj/of7Y+t/h/Qvg2q+hZe+DM6/yENwPS76Edb+Dzw/tzob254Lf+4pXWVqfpn/c9UqY/LRemLilTd80TKjqEIlEmD59Op9++ikAffv2pW/fvgd3Ui7wfm1HGpvnJQqTGOFSmPAQ3PRT5c6pvOSvgZEX6xeiuR9Akx4w5HMv87qqsG8blBQos1Zm9cTzhdth2XfJj+sLwKn/KP/8qghLly7lggsu4OKLL6Zdu3YHezpJ4QmUI40lX9qfz5sFBRuVnftQQEoY8yfzXe3muTD2b3DVR5U+LQ8Nm+fDjw/Cut/U48wa0ONaOPMh/XWFW0FG7MfKrgUle+KPq9dXYcOHkam2c+fOrFmz5mBPIyU8gXKkUVro4pp9FT+PdLHuN1Wyw4pl3ylhU6dVZc2o8ijYqKKblnwFZUXQ5DjodSt0uehgzyzO1kXw3rkQLI4fKyuE6a/DVsPnFqgGCGz9ISffCRMfjT++bTpUrwerfoaVP6oclpYnQ8eBEMhM5yvxcIEnUI40jjrW/nxmjUNr8d2y0OECqRYus9e0eT7MfBu2LYKsmmoh7n4NZFSriJmml12r4d3++lDajdPVvx33Q997D97ctEx8TC9MtKybrH8sw5BVw3pD4wtA98F6gVK2Dz69HtZPiR+b9Q7Ua6vMnU7f5XBQRTpm5iTmvngkjRc2fKRx7GVqR2fF8deb27erKlk1XFyTm3hs7kh4q6/yJ21ZoDSd7+6GdwfoTSpVlfH3W+dlTHoCdrqJjKpgyopgxXj31//xjb123OdulRmvZfyDemESY9cqGD1EmUTNCIfg12fg+Y7wSg94vgO80w/WmYzl4RpPoBxpZOXC4NFQzcRR3X4AnPWfyp9TeehwPvhtTBs1GikTiJbd6+BbQ6XbGFvmw8+PJh6vShTtdF6oF46unLnYESwhqXDeTXPtz4dK1T8ty7+3vn7bIlg72fzcN3fCL49D8c74sbxZMOIi63s8HPEEypFI855wxzz9sfOHKUETyDo4c0qVGg3g9H9Znz/n8cTigfM+si9OOH+UCkOuqhTtxHGhLtxWKVOxpXpdqNXC/fWRoP35gvUm5jOH92HzvMRjWxZYRzpGgiqAwEqzSTP5RWW8NXk1d46exwNfLmLq6p3INDz3uHHj6NChA23btuWpp55Kw0zdUaECRQjxDyHEEiHEYiHEKCFEthCitRBihhBipRBijBAiM3ptVvTxquj5Vppx7oseXy6EOHTSRqsy1WrrHzc7Eap4lzlL+tyjIn3QzL9GI7jqY+h6eeL1u9fajxcssjYnVQVqNgG/g+Cve3TlzMUOIaDXX9I3Xm4TCCXZttbsfVrylf09WxaklguTJFNX7+S0Z37hie+X8fX8zXw0YwNXvz2DWz+cS1nIIdrNhnA4zF//+ld++OEHli5dyqhRoyqtOnGFCRQhRFPgDuAEKeUxgB+4CngaeEFK2Q7YDQyN3jIU2C2lbAu8EL0OIUTn6H1dgAHA60LYFQLySI1DONNYCOX70WoivW+HjuebX1+jkf14vgBUq5O26aWd7Joqq9wKfxZ0G1y+55ASdqxQi2uZhVPdDSfdCsffUL65xMiolqg5mplutWyak3iszEWko5trysHuojJuGTGHwtJETXnckq28MnFlymPPnDmTtm3b0qZNGzIzM7nqqqv4+uuvyzNd11S0ySsAVBNCBIDqwBbgTOCz6PkPgFiM46DoY6LnzxKq8fEgYLSUslRKuRZYBRw+QeeVwc6VMOlpGHcfzB1hHjrsFP9/KBAui/9tt5N1Wmw7DzJ35Fclzvk/aGQSsecLwMX/g1wHoWnH8nHw2knw2onw5mnKYf3TIyoiKll8Phj4ojKnlpffnoPfX9IfO/YK+3uWfA578vTHGnezvyejOsz/CF7qBs+2hY+uSHutsM/n5pkKkxgfTl+fspayadMmmjeP55E1a9aMTZs2pTRWslSYQJFSbgKeAzagBMkeYA5QIKWMvZN5QNPo302BjdF7Q9Hr62mPm9xzACHELUKI2UKI2Tt2VGFzRWUipbIHv3qCivyZ/rpK9HupK2yYbrj2MBAoWux8IEcdoyKGzKjZFPr9t2LmlE6q14WhP0KXi/XH/zIFjrk09XGX/wCjroKdy+PHSveq2m9f3Za6b6FeW/fX+myCLOaO1D/OdAjxlpHEyK0ul9hrqcIH0/+nzF5FO2DleBh5EUx/w/65kmDp5r2253cXB9m2N0nzXhQzH4yoJHN2RZq86qC0i9ZAEyAHONfk0tirN3vF0ua4/oCUb0kpT5BSntCgQYPUJn24MXs4TH0l8XjxLvjIYDKpJCdkpeFkaz/zIbjsPVX3K0bznnDzRLVYL/sOFoyBHcutxzjYZFaHhl3ijzNyoGGn1MeTEib8B0vz56JP4L914MnmamPi1ChLS3G+u+sycyBSZnOBYeNjF1xxAMMSklkdrh4DOSbrRI1G1uau8ferZNI0kJvtnAKYk5VammCzZs3YuDE+z7y8PJo0aZLSWMlSkSavfsBaKeUOKWUQ+AI4GagdNYEBNAM2R//OA5oDRM/XAvK1x03u8bBCSpj6qvX5UsMOKVJ1+1S7wigQrZLpYggBx1wCtVvFjzU9AZZ9q3ITRl8NX94Cr/VUdcIKo1rvnjyVv/DVX1XS3q7VaX0ZSbN/d/zvjOzyjbVzhfpni1Tfnbkj4O0zleAdcZEyDT3fUZWa37Ig8bZFnyUeMyM3yYWvfgf73ioIkKHEcOMmxyVGOp5wo6onZoUMw4L0hGNf0M3+dZ7atj51c1LL9D/xxBNZuXIla9eupaysjNGjR3PhhRemNFayVKRA2QD0EkJUj/pCzgKWAr8Al0WvuQ6IeYvGRh8TPT9RKt1tLHBVNAqsNdAOmFmB8z482L/bOZpJy6Fu8jImIwZdmgu0hSPXRpMbjcJ29UT46FL48SF44RiVvzD/Q5j8rOrXMeWF8s29PJQUxP/OKGdCqpMQNlK0QwneNb+ov/dtgYVj4K0zYPEX8eukhKUOkVUxdiXpjM6qAcdfZ3OBVKa6YZ31c4JEP9mC0TgGp+xLz172hJZ1uNBCqFTL8PPvczumPHYgEODVV1+lf//+dOrUiSuuuIIuXbo435gGKqz0ipRyhhDiM2AuEALmAW8B3wGjhRCPRY8Nj94yHBgphFiF0kyuio6zRAjxCUoYhYC/SikP8e10JZBsPsmhKlCkhBlvKK1By9ZF6pyd7XjfNn146LZF1tduWWC+80Yqh3XDLtD+nCQmnib2apyt5c0hqt9emZzKiso3jgzD17fB0WeoaLntS+N9d9JNsAQGPK2c5nYbqOKd8PlQtYFo0zd+XPjVfMGdQK2VnqKpQgiGXdGNtg1rMGLaOnYWKjNfn3b1uXdAR45pWqtc45933nmcd955aZhpclRoLS8p5cPAw4bDazCJ0pJSlgCmsZBSyseBx9M+wcOZzBw4+ixY/bO76w9VgTJlmHlm+/Yl8MsTcOYD5veVFcOIC9OXbzL99coVKBtnwfj7VHZ3jMJtqnRJRo7KlJ81XJUgyakPXa9SOSF20WtzR7rX7JwI7ldmrp43K59dRbHsG6X97C9wvlZGVCfINn3jx3wBCLvcn/oyyh+OrSHg93HHWe24re/RbN9XSk5mgFrVM5xvrMJ4xSEPZ858UDXOMnNQt+gNG6bFHx+KAqVkD0x+3vr87y/BSX+BHJPaZYs+gR3L0jeXLfPTNxbAvq1KU6jVLFHz2DwfPrgg8XMt3QcfXg71jtZngpcUwC+PwR9j4YJhMPs9ZcaTEdXk6uQ7lKY2Ls0FJfdEHcOiApeZZPunrJ8Cw7oobaR2iyS+90KFP9ds7HxpkgT8PprUPgQKkrrAEyiHM017wLVjlV9Aa87pcS30fwKebBY/lg6BUpyv6jH5A9CsZ8UXmVwzSWW1WxEuhVU/QbcrE88tH5feuWTWUAJgy0IVOda4W2omqI0zVaRVTNhn14YTh6o+67Fy7L88bh3FtnGa+mfG1oWq+KU2MmrRp7D0a6jdMvm5OiH88M7ZkFfFXJ57o3kp+11Gngk/DJ0AzY6vuDkdJngC5XCnxUlwzScwTBNOeuErqtqqlvIIlFCZ6vQ4+z21iINqhNTnHjj5b+kt6VKwERaMUnWd3Jg5Qhb5KE51o5Ilt7FKAIxVy/VlqKirms3g2Euh559VhvuBeZUBUi90VvyockC0LsKSAvjteZWcesUIZUpaOSH1eZqF2YbLkneGO+ELqO9DyW7na516oBxscuqrzZmHI55AORIwCwkOG8IoyyNQvv27yizWUrJHCRmAU+5IfWwts9+D7+9xmXsQpZlFUYXmvZT2kg58GYm78EgQSoOw4w8VXrz4C7j+O6UlTH4u3r2w5SkqybLRMfDJEL0w0fLHWKW1ZOZQpRffGC1PgbW/HuxZpIfCbfDTw3B2Fa9CXQXwBMqRgNkCbIzLT1Wg7FyVKEy0/PYcnHhT+c1fG2fBt/8gqcW0bT9o1DnxeLBEFcf0Z+rLtaRCoFqicDZj+1IVZbRmkv69Xv87rJ+qkiqNn4mReR+pzoRVnTZnwo4kihEKn7UgrSr8/pKqStDkuIM9kyqNJ1COBMyERboEyioH80vJHvjhXpUvUO9oOOayxErHbpjxP5LemV9kUiojfw2MvCS5HB07Og9y33tk9USLExI2znC+f9McKNxif00gO/mKvOlmjdXrtKCqC5MY8z5Kv0Ap2qU2ZFsXqgi8LhdDqz7lNhPfeOONfPvttzRs2JDFi21aZKcZrx/KkUBFmrzcFAycN0KF1X53N7zQWdWLShbTHBAHjL0wIhEYdXX6hAnA2knpG8sJN7kcl72nfFdm+CqgSLex10xVwam8fyoUbk3veGsnw8vdlGl40acw+134YCB88qeojy11rr/+esaNS3PgiQs8gXIkYLYDNH5hUxUoxm6ITpQVwSfXJd+iNjMnueshMZR37STl00gn+9K8yFjhz3InEI46VlUhzjB5vyqivE4qFYgrAzdmyGRx6k+fDMX5qsqAWcvjP76Byc8kHk+C0047jbp1HUr7VwCeQDkSqEgNpenxSkVPhnApzHonuXs6X+R8jRFjKZLNac4VidGiV8WMq+XCl1WtMSemDIPPrrMPp65MfBYaTGYVbw9gxnHXpm+s+R+bC5MYs4aXW0s5GHgC5UigIjUUIVQ4a8tTk7tPm+HthhOHQr12yd2z6mfYpnEOu+lxEkgywSy7NlzxoYoaqyjqtYNuV0Gv25yvnf1uxc3DSLaL8iCNTXq2XDUKOgxwvtdXhVy8zXtBg/bpG2+rTZkfUDkyaaobVpl4AuVIwI2GUh5zSPW6ycfpZyS7cNeCG75X5WTcsmYiDO8HedGufR3Pd6hMi0Xeio2DtPlJqq/9jeOgvYvu1KkUcIzd07oPnD/Metdf2fS43ty0pmXT3MRjWTWgg1knCw3V6sANP0D7ASoK7GBzvk1FhlTQ5iRZcQhqcVXgk/KocFxFeZUjt2HXapj6cnL3dBqY/PNsWagv1w5w2j/t7ykrgh+i19RsAqfcmfzz1m9vvVs+KroDF8K5j3tWLlz/vb5kfuPuqgS7HbU0/eROHAp/d9jdVhbbFiVfoRjUe9npQvuIqXptVSj11WPgP/lw5UdKCw5Ug4wazs9R3srLWup3UE3Z0kmXS+zPt+lrXjKoiuMJlCMBM+0jXWHDkFgW3In6HaD7NcndM+FhVUJ+s2HHu+x753s3zYkHAZz1H1WdNhlkRN+fvm2/+N8rxsHWxfHrrKjfQQmTpsfpF4pGXVQBRTvqHq0i1GJUQD2plFg9kZSSLH0ZKjpsyBfRjpMmGmBOw/jfQkCnC+CG7+DBrXC/pqVvwKIHTLuzk5+XkZg2W6Oh/XWp0KKXCqE3I6NaubuGDh48mN69e7N8+XKaNWvG8OHDnW9KA55AORIw86GkM1O+xEUJlBidBsH13yqzh1vW/Q6/v2h+bvsSd2MU71T/C6Gq7iZD4Xa92UWbYb9tMbxxCkx50T5J8uTboXFX9bf2Ol9ANXayCzqY9orq7R7rFui2WZVb/Jlwyt/TO6YdMflRvS5c/r7ywRnx2SxNQqg5g16zbnZi/G83/jJHomNXROFUIeDiN+GMB/SdI48+E24YB026l2v4UaNGsWXLFoLBIHl5eQwdOrScE3ZHFfJ6eVQYRg0lElbNkLQk86ORUiXPBbLVD6OhSTa6FRe97ixMpFQZ5SvGKU3KrdCwQvihTuvU7w8WOYfH/vSwKjdixbLvlZmvWh19HbVIWIUDX/YezDsDvrEwyW1bBB9fCQOehC8cNJpk6XCeCi7IqJ6aCStZjFqFWdtiO1/X+mnx76t2Y1S/fTzYI2hRwy0ZYs9RUd1M/QE4/V9w6l0qxyUzR30/DmE8gXIkYNRQvrgFFn+uP7biB+h+tX2GbmmhKqUyd6Ta8VerC8cNgd63Q/X6cS3AjpI99gKltBDGXKMESrroNBByG6m/N8xIbMblhAy7W1TW/259bsUPqmvgBS/qC1PGggB8PuVPsWP7EvjxwfTvmJd+7b6jYjrQBhUUbICxJrXezHrQy2gzMyttVevnKkujYHRbO27THJj6CjQfAlvDyvGe09C5NbM/oNoUHAZ4Jq8jAeNiuPgzEmzff3wD0yx60IfKlEP8vQGq3W1McOzPV874j6+Ay4abR+MYQ32NrXqNjLs3dWHiN+nB3aCj2gGWFcMf38J758LqJItCxjSy8hIshq/+ooTmgWOanfR2F0mXWxeWfx4JVHKxyVh2fckeeP8C2DA18Zr1UxKrI6wYZy1MQN+9Mh15OAfMai42E0vHwvBzYMmXIEPIcJlqLLZzefk7YFYAsjxBODZ4AuVIwK3K/tswfce+SERVxn2hM7zZxzp2fst89eOvbohK6XEd9P6r/pidQCnaBQvGuJurGec8pu/G16Cjqt311mmq98tnN6ReN8pJELpFRvSRalqBUt5ClYcKMU1i3oeqDYEZMvrd0+KUDLtVU7NKq6GkYu70BeJ+GKffT1kxjL39gCaTvWcNu4pCatGWEaWFVdACngpSSnbt2kV2toPmlAKeyetIwO0iuj9f1b9q2Vs9/uGf7jPaF36SGDm2vyAxgdFuYd6+NPk+JdXqxBfoGo2UPyCm4Wg7Msqw+1avZqRLoIDe7q/VfGo0St9zVGViAmXFePvrVoxXC3HMDLtzhf312lpbWkF94lDYtQbmJJH0WbdNXLNwMjEu/173/Wg292nyuJcdtdpwIAJhW1m8QVoVIDs7m2bN0m9m8wTKkUAyTsXYj2fHiuTKoxTvSnTo/vF14nV2C3Mq9bpa9FY/aFCmlLQVQPSpOmXrp6iHB3bSaW4GpTV/HewqwZVFzOTl9L00+i6q1VW7fTdov4uBbKibpJaycwXUjOb/OM1zrz6jPaOsgNbT79Nfc+VHKvT5MMczeR0JuNX4+bdUAAAgAElEQVRQsnJV61pQDZ2SoV5bd87LyU8re7OZCaBxt+Rb0WoXYV+gfOU66rVVjvGT71Q5ElrtKpmmXsmwfanyT0HlRFiVh+xaKsy1vMQ+I6fCoi1P1geJdDVp5WyF9r3csxFmmLQycCLmE3T6/dRx8Z11c40ZUqp+OZOeUsEkeXOqlPnMiCdQjgTcaii9botHYCXrSHSbqLhrtSrP/dPDied8fuj/OJalTmo1Tzym3eGXR6AEqsHts+HPv8KZD8Fn11tUrJWp9Yq3Qobh48vh+3/B70lWG6hs6rSCdudYnGvtPlIppqGccCNk2ZQgOdWQG3PCDe7LsGh9KFNf1Tvs3VC/fdwp7/T7aT9An4hppEmPeEWFZCjOV0EL750Lk56EXx6Hd86Ejy63Lyx5EPEEypGA0QZs1ha35Slw+r3xx8kkVvW+HVqfltycfn8J8mYnHu80EAaPNvmBCrXTNFK6N/63P8O5VpcV9dvFd8Nf32qfrOnUWTFZ9m2FmW/qS+vnNnF3b6olRpKtpQbKV1W8K/F4qz7wtznKJAXOzaFiQr9mY6UJmr3W1qfrKxLE5tzmDHdz1WooqQRinHpXfJ5O9weyVD6J2Xcvp4HKvUqFL26Om1y1rJpgna90kPEEypGAcYd13jOqZ4aWDufq/Q8dznPX/+GGcUqrSMX+P+9D8+MdBsAxxlpHFmq+1n5dHg0lM6qZ7S+AJS5yMiqiHIeWfZvd7cZTfb1uKhcbKdigtEsjtVuo704sYTGrpv2OXZuH0vxE+PvCxE1Ooy7m9/a5G0sNVjtuqtF8/kzo9wh0Hxz/PThpKCsnwLj7zJ+z3yPmiZtObFuir8hgZPEX7v1JlYgnUI4EjF/0cDAxmsr4o/FnwNWfmJuZdGNHtZ9U7P97TcpzF+erxMkN092NoVX9feVwysfu2zDNXaRZzaYmx8y0inK0cnUjLLQamh3GDGxHn5DFvM1MoTETViyBL1Rq/zkYz/kzoLbhe2YlTFudApe8ZR7A0etW6+d0otGxMPAluOsPOPUf0Tm4ECjhkErMtPrO/PRIahqtY0toCRtnJj9uBeMJlCMB4w8iHFQ7cS1moZENOkDff9uPPeIitVv68cHk51W7hf7xtNdhWCcV02/stmiJRnPxGzSUZMxfBxywLrLQs2ubh/jGeszUPRr6P6EWqM6D3M/BiNtuiLVaOF+TXVv/OFbS35IkHL+xdruxXjKhEnsbv5lJzCg87QRS1yvgrmWJn4GVVuOGRp3h+Oshp75mDi6c8msn2fctKdqh+vIki5sWxlaFMQ8inkA5nIlE1A/buHsKlyX6CMwW0n3bYKyDrTZSBp8PTS27vYfGfLLoMxh/X/lCZ40mr2TMHjGB0vwk84x7Lf0eMb8mVkGgWh2V0Hn89fq+F8n6d9xoKBk5cK5D9eTMGonNsNKZRGnUUADKCs2vtcL43ji9V9k1ExNpy9Pf3uy9dqOhFG53HrtwW/LzadvP/vPPyEneb1kJeALlcKSsGH5+FJ5rpzLExxli4ssKExdus1DE+R+BdBEum0ptqTMehKO6qqKJIy6GL5OsAGyGL0P/I0ymQVFsR5xTXwkCKzJzVLSR3eKljQLT7iIvHZ5c8mLuUc7XNOmu/F8teltf0/OWRBNR/TR2H4wJ12S7XWoxaiRuTJfGBXd/MsmnBi3J7Plix+w2Jk49cADqubjGSG6jxCoTWvrc5a5JVyXjCZTDjVApfHgp/PZ8fMdsFB5mvgujUNi/O9rvIs0IP1z5IZz+T/j2Lhg9WHVWTDZD3gx/hr7seTJdJLU74nMeV2VjzPwIsdpkdlqMVthor2vUObkyIG4WjGYnKhPSlR+paCstwgcn3qRyR4zzdQoqcNPeN0ZMgJYnnNooHNxoc0ahblecUw0a/9Mo2G01FJsNU/Oe0NDG1BbITj3y8KxHVLSZloxq6vvZ5+7UxqxgPIFyuLFglHmxPS0THko8FtuFSamSqJ7vBOt+S//8Wp2qNIcXuyZXCsMNPr9+YbDrCGhE6wQOZMKFL8OdC6D33/TXxRZNOw3Fb6GhjBoMG10GG4CK9HEiNo+ceqrPzM0Tof+TcN5zav7nP698S0Z7u1UhUFDNwC4fgalANVsgD5i8yqOhJOFDOXCN4TNwND9ptHCj8DMTKG40FCHg0rf1PU20hEpgxCBY86vD3AxEIjDpCZj+P/3xOm1UQzan0OyDhCdQDjcWfup8jVnUyYZp6v8pw1QSlWlv9TSwdRGMHGRdFLA8GE1eWqd2zab2SXRmC1idltGOgobnAH1PEyNFmoVNu3Dlr7a+J1WMi3vT46H3bWrR0QY9GBdQOx9KwQZodoIySWppfTrc9HOicIoJ0FR34mCiobhYmoxCXetQN0WzCBvHtxMoThFxjbrAVaOsz0eC0bDiJAIdJj0Bk59N/B1uXwLf/sP9OJWMJ1AON/ab9JFww/ppsHYy/PJEcvcZHaNOpDo/Nxid8toS5h3Ph442tZSsFjDjohVbZPJsQja3LFABDeAuWqc8uM1DSSYiKLRfmUWNJrfT7lG91Y1FDn0BpdXOetvd+EtNyvqkw4fSvr/99dpMfmMEndnzuXHKx1jroIFsXwI7ljuPAyoCc9pr1ufnfwz5a92NVcl4AuVwo0HH1O6LBGHkJS7yE3x6Z/eAp5Kzt1ckfoOGslRTnNIXUC1nrbAUKCaLZ3G+KiFjhYzAwtHqb7d5IqniNu8mKf+GUBsF43viz4xW/zUcX/mj0mrdRo7NeDPxmPF1pOJDadDB+tqaTfUdNY1+xVRNXjHcVKN2+13YMM0hr0vC6hRCkSsBT6Acbpx4U+r3unGMn/UgnKzxK9Q9Gm6dlpy/oqJYN0Wf5a7tO7LoU1jyhfW9VguzcTfuz4gGNTiYL3bHTHoVXMivIgRKu7OVT8Y4dsFGeKNPYvmVNUkGb2wz6auTDh+K8TEo4dfrNuVbqtk4ftxo9rVzysuIs7nKqVaXP1MVH3WDG43ILlDgIOIJlMONVqfY+wrKS8le/SIbLoNaTfUC5fL3oe3ZFTcHK2a8AdNeMT9XtMM8ui2G1Y7YTENxE/qbG128nCoNlBfXJi+DQOllEZJavZ5KyoTE9+T7e8yFQbLVb83qj6UU5WUihIzaU61mMOBJFYKdpdGsjYU/7TQUgLW/wZgh8MKx8FovZRqOtSnePF8l99rR9Qp7DVlL85PMhaOW1n3szx8kKlSgCCFqCyE+E0IsE0L8IYToLYSoK4SYIIRYGf2/TvRaIYR4WQixSgixUAjRQzPOddHrVwohrqvIOR8WlCc724mZb+sXkNUTVamUPXnxY61Pdy5NXhG4iYqywrXJyw81GqhiknZ0vUL9r/FDVEjVcbeOcKMPpd/D+npuwgfHDVFO99hrMy6ydgUzk6FwG8w3OLFT0lCM9wQS349YjTbQb7TCZeic9KY+FM13YsRA1SZ7zwZVxPPXp+Gtvkojfrc/rPjBep4tT1GmYbfUaGCfC9Xh/NTqg1UCFa2hvASMk1J2BLoBfwD/Bn6WUrYDfo4+BjgXaBf9dwvwPwAhRF3gYeAkoCfwcEwIeVhw8h3JO4OddkQxgkWqq2OMyc+oUikrf4wfy6hevqzllClP3Sy3Gkr0ddk5+Ou1i0dYaT4HV5Ge0TwX17InVZOXP1Pfo6N5Lxj0mr4RVdqalRmQEfj6r7BtafyYUaC7KoxpEjBhvE+b0Gk0JWk/ECcNxYyC9eq7b1fd4eQ74bpv9NqRG/o/AceZFOLMqAaXmPigqggVJlCEEDWB04DhAFLKMillATAI+CB62QfARdG/BwEjpGI6UFsI0RjoD0yQUuZLKXcDE4ABFTXvw4LCre4qBWtx2nFrcZPwaCWgajYjqYW/11/d98Aoz64tGac8QA2bLPZdK5WTGnSawaZIPCLul3BXdkZMFplqquaW63fIqS1uDKOGIoTeiW624Ll931NBhmH28PjjVDQUo8lL+BPvy6iuVMNfn4Xx9xvmoPFDmAoUN8U5HfqSBItTE8yBTBj0KvQ1zDmjevLCqRKpSA2lDbADeE8IMU8I8Y4QIgdoJKXcAhD9P5ay2xTQNrzIix6zOq5DCHGLEGK2EGL2jh070v9qDhV+fQY+GAg7XYYoxmh5MnQxloy3wKlO0x/fWP+IajV1FxXW6BjVF6X/44mVcs0QPpZ3SqEku+Z+U6wEitNi8+vTsHudzt+UK+I5BS+FLuPx0JCE28r8akft2jw26x13lZmNGquUsMe+6VRpJAnBn5FC++ati+N/p+JDSdBQAiYaSg2Y/S788phDgqJN2HB50AaGpEJWDf3jZBvfVTIVKVACQA/gf1LK44Ai4uYtM8y+vdLmuP6AlG9JKU+QUp7QoIFF1urhTt4c1dUtFaSEy941V7OTZc0v1iYvGXFXALLfI6pGlRCJAqV2y4QFKOivxoCvBV+ETiElLE1eAfPHhVudx/zyLzrNoKaIh4LuoxqlJL5Hu6jN0khL94nQMqI2EU4YTV5vn6lyI2KsHK+aNmkSNvMK9I7rvdIiE174odtglxPWoN1YpKShuDF5VYcpLzqPZVoBOQ0Cpbx9c4zRaKGS9Dd4SyMVKVDygDwpZayw/2coAbMtasoi+v92zfXakJhmwGab4x5G5r6f+r0yojoi7liWnrlY7eDdCpTPhsKK8TDx8cQkrqzcBJNeURAkgidDV5uP51QM0e1uNPa63JSW3zDdMj9hr6xOmYlAIZDBP4O3sEcm0YlxzS/2mfuQaPLaPDfxmjnv6zYkRYYh/1z2D9ZHTBbI0++FeSPdzVWLtolaKnkobjQUGVGOdCfMVMJ0aCh1WqvyKeMfUP8X7Uzu/qDJb+Xl42DB6PLPrQJIsd2bM1LKrUKIjUKIDlLK5cBZwNLov+uAp6L/x7LPxgK3CyFGoxzwe6SUW4QQ44EnNI74cwBD+VwPQJlYUmXLfHipm7vKwcJvbz44+kzrWHq3lYlL96jaV2bPs21xPCw3ShD14w9jvgjsz25INWz8DW5VgphAqd3S/joAJCz63PRMDiX4MxOz1xvUymVjVjvOL32Cof4fGOibSj2xz356MoKjG99tHsrMt1XhwawaZGfqzX3zZVvOKBvGKxkvc75fUykgEkoMw3Wi5SlwzKXxx+nwoZhpKK5NcSbvny8N++3x9+vzu378D5z9X6V912qe+BpK96nPYNFnKqrObEOydxN8+Wf1GzvumvLPMY1UdJTX34CPhBALge7AEyhBcrYQYiVwdvQxwPfAGmAV8DZwG4CUMh/4P2BW9N+j0WMeRgyLbFJsnudusfdnJda30hLIhk4XWi8I2/8wP26GndAy7PRC0b1RyOIrnZ3nUDCz0KXfLbbwOdaNirJwjOnh9zKfJsufKCUCGZnccVY78mRDXgtdRE2x31nWtejtHFXntvRK2T4lsIEW9fXO3yABIvhYHjE09Cp10fsk9n3IrgW9b4drPtXPOaXSK4bXbOaUzz1KFbt0Ip0aSvOT4n8ndEYtU31/Xu4OL3RR5rhYkuL+3TC8P/z8X2WO3LvJ3l858f+i3VfDsHyc6hz55a0w54OD5mupMA0FQEo5HzjB5NRZJtdKwDTbSkr5LpDm0rSHId0GWy5gpvS5W5W5d0JE+4W3O1v1Ydi1GhZ/Zn7tUV2VI9pqgStPAy0thh9qOCpIrDQUgYOwLHLZBCkmUJyacB3A/Hlb+7ZzglyceMKfwdBTW+MTgm3jniVTuOhHYyxxbkYymfLRzy47U/8ZhqLvbSEGX4o21NiKP09REWzV6ydWH4D0lK83M3ll1YAz7oNPr3cYzExDSVGg5Jq1gjahcCv89LAyNZ//vEqW3J5ELtW+LfDpDWqcvFnx4ws+Vv2Qul8D9dpAh/PK78txiZcpfzjRpq99QhQoE0CrPqo6au/b3Y3b8mR4YDNc8QFUqwvf2fRiiDla3ea1pInwAQ0l1Z7yLud7QKCU//UdEzGJxPNnsmzrPmauzacRuxLPG+lyCbQ/x/k6twIltzEc1U39neAHU6rSUQ0M2ln7AfZh1G37qV4wNZuYCxOz50oxsVEaBMq+cKbSqAe9roSZFq3QMtNQ3FYhMJK/KrnrZ72jknKNyZ5uWPaNXpjEKN4JU19SgRbDOsMvT1ZQZq2eCtVQPCoZIeCCF6HpiTDWorTG3xepOk0AG2e7G1fb52LSk/YVg2MF8FL9MbpGoN1V1s2tjr9MEJYWe6TsWvYF/NzWWYoJEieB0rYfrPrJ9pJGJDpotxaGufR/UykuC9Pc76JUR3uXKVlu6kMBnHF/3K5voSW0atIItEnzWTXgihHw0WWJBRDrHg0XWpTD0ZKG4pDbi4L4i8rQ1r8eO+EnNheeyt0DrsZ37GWqCVdpoaq99capcZNSKl1HrUhl3V70mTI3VgSRIPz6lDLT9ry5Yp4jiqehHG4IYV/GW+todNvgav/uaG/6sHPNoli9LKOzMd0YGhrlVq/G17efcsD0lUBPhxbDbnJdIL7wGU1et05VnfTO/j/482Sm9nqTlZGEdCkdTUj020xYnk9xmVr8vwqfSpl0eB/d+HJmDYePLtcfq15fr5XlNFCbkR7Xxo9ZaAn7jOHD/kxocRL8daaK+GrVRwVmnPss/Hmy0kycMNFQlm/dx+uTVvHyzyuZunon0rjDNmiVw955nzphvVZ3jf8nzp5+He/9vEBpaUefCZ0vVEEVWh/dvi2Jc0q1Z09JCrkn4dKKrcEHUX+Ny41FingayqFOJKIy11f+qKJtWp1qHyKr3fltsOnpoSVvliqMd9XHzo239myC34ZBk+PdjW1HRnVVdnzXSv3xOq1VgUZtIyt/gGOa1kLiIyIFPmFYfLoPhtrNlOq/zyTq3G1WuJXJq1EX9S/KuDc/5iFhH91u5mvfsCfuM9lBbR4I3cjTgbcTX49xPlYs+RK+M/GxFO+EWi3g3KeV9tbsxERzlMV7UhA2mM9i5rSajZWGkwoGjeT1yWt5ZqW+HH635rV5+9rjaZgbDTAwfAb37n0Cn+FNlRK6+1azcerDlJ7xBVkBv9r0fHwlBDXf5RlvwL6tcPGbkBEd31hV2d0L0de1c0uTHir0e2YFllXZm6ciQVPpce8ST6AcyuwvgFFXxbstgipnoW0kZES76wwk4QdYMwk2zVU1qgrs4vojKkrFbda9HTWbwG3TVG7ElBfixzsN1NcTA93CGsJHJoadWCBL7b67Xa2imCIheCceG/LF/C38un0eF3VvSt8ODRBWoVWxXbGDz6XXlpFkWAkBG4w+oE/DfVkdacLQwPd0962mTkaI6iGN6c7O1yAlTH7O+vyeDWrR7Hie+XmLsRMEioNQKwtFGLtgM2MXbGbP/iAdG+Xyp94tOaapdWLjlNW7MRbEWLCxgFtGzOHL205Wn4/hnjoiMSIq9jH2j0xh9fo8OrVpocLRty5MnOjSr5TGd340UCWZpmQxMqrrG7u5uidHfafb9lMmuW0mwRrpoqLqs8WGr9DRPSqWb+7UC5MY2h1SpqF0g3Yn2LZfcs+3Yhz0vMXdtUu+TG5sM7pdpXahbfrqj/szTVq4xhd400ivWOkRfwCadCdi0KDW7trP1/M3c8P7s7j943mEwhY2dYcorykrd3Lj+7PoJU0WLBcETfZ4c2V7/hr8O6eUvsKCljeYz8eM4nznxWnNJOtzFmPnBw0CxSauubgsxJDhM7jn0wVMXrGDBRsLGDN7IwNfmcJHMzQmJcNzRSyWpvkbC5ixNurDSyIwIlOEyNq3TnUl3TLf+sK5I+Nl6R2b1Zm8btvGWBa0Pk1teLYt0WtN6aZ+e5f5U6njCZRDlYKN+o6EVhjj2LU7lBOTdNBFQnDSrdDxQhcXly+iJNiou3ouSOyf4c9M3GlpFhdp9kOfPRzK1I9dSsltH83Rndb6Xr5btIXhUyxarB7woSQuZu9OWcuQ4TOYuGx7wjkzIiZvkZlA0TJptSGwwM557SZZ0+4a07ElzQoNC/Ky7y0jiF76eSUz1yYGcUjgoa8Ws2ZH9Ptp+DwtgyuA2eui47mMzItNrUXTZrDRwcwbLo1rL47trc1ecwrf+7ZnqbJJIy6EfJtOoOXlxJvcJ/CmiCdQDlW2LiSlL692kXDjLNXS8hS1w7/49eSf1wURCZtlXV4KXczjDZ6JF8ZLECgZpk5chSQLk7IovzwOIwZBWRHzNhYwbok+78S4Ix45fX2iExjYXhhk+96SBIGyMb+Yx76Ll2P/PXKM9QuN8nRoMKVSP05Q2psk9gSTSACsVgcad7OfxNEJKWEHKAzqX78gwjOBt7g131A7bPRgfht2NS//tIJte+N5RqFwhDEzN2JFRMKYWdHzhs/TMrgCmL5mF3eOnscXCwyfoRTm+YkClvvb0/uttTz1k4te7DFTl/a3kmw7CLdk14EuFynTtWP77XLy6zOQv6ZCn8ITKIcqmSlUdwV9lFcy5cnrd1DlIoL7VdRQBfBBuD8nl77KC6HL+WxRQXxBzzBGFWUk7p6ju9V+vrn4hYW5Km8mTHuNsfMTneURg1aTt3s/pSE1jlbjefnnFfR84mf+/LHepPXmr6t1GscboQstI7SC0s8dZbfxZnhgQoFIJw0lYUw7k5cQKurKivrtTasehCOSx75dylu/rdMdH+ibxhWBXxO2MVJCn33fs3ziSM54bhKTV6jotYL9QQr229c8W7sz6m8wCEYrkxfAlFW7+Hr+ZsYv0zvNXw9faLoBL5aZ/Lv4GnbsK2V8qMeBOZtSoxE0PT5xThXV4vqcx1WTriJ3Wm25KN4JEx+r0KfwBMqhSoveCaGzSeNW/T3qWBjymdpBjbxEZfc64abchQGtQ7qwNEQotkIbhaeZD2XXKsq2r+IK/yT7J5n3IbuLyxIOG3fE1TL8ZPp9RCJSJ2xEdDmdu0HfvfCLefrIniWyFTcF72ar1Icjr4405uKy/zI2cipA0gKlFJMOknZ0PB8ueiMxLLrlKfCnr+IRTRqGTVjOO1PWJmhL1wR+BhI9B7Gv0dX+nykuCzP0g1ms2VFIbnaArID9EtMgN7rzT0JDiWEMYHgudAWbpN5M9Wu4K5eXPcI8qfr9rJWNGR3qa/3VP+OBuPapfW8rSnsYe5sqoVJZLB1bodWKvSivqsb2P5RDu2SvWsi7XKxKcBsJZMHZj8JXt6b+XEJgTBA8QEYNOPZSOPYylVcghAoH3uBQEwuUM/OCF+G95PqgaReI1vVzyPBHFxWjhgJK29Cyey0Zb/Sig1Mzz72bad8tsUGRcUd8ftfG+HyCX5Zv5xQpiJXd8iHp75vF4xl6Le0z7uVv4nZWy3hk0uRIN04tfYlTfYtpIArYKBsyI9IRqXmuRIFiLyDKjD9ZNwmk3Qcrs8qaX1VyZ6MucJS5SW5vSZB3p6wDIGwQHS2E/S66pU+ZoIJhyQWvTOHTv/Tmgq5N+HyudRjtJT2iEYkGwfiXM9px31TB3hLrhTxkeC/aN8olf3cuTUVcc7kz+FcK0H/eD4SGsptchvgnkCuiJrrcJirkuYemfYNWCy5PySB/pqpAYJXXYpdwm24iQVXnK5lSPEngaShVhUhElTR5vZdqzjTjf/D1baoC8KY55vd0vxouey/Rx5AMVlu1YCEs+lSFIMeumfeh83gDX4K//B5vgZsE2sX02t6aaBTj61s5AYoSkwJFJEhz4VDksXYLLj++GVkB/evWCpScTD/3nKM0rG/mb9YJgFZiK69lvER9oc8I7+xbz6jMx6mNPts5RIBJke58Gu7L9Ehn3VhAgg/FuEgaSeih4raAYUY16DAAul1pKUwA5m8oYH9QhVwbtYTtsrbtU+zQnC8uC3Prh3O56+z2NK1t3kel01G5dGkSTeYzCMbzuzbjqUu72j6fUfi+c+0JCXM2K8UTxs/TocGcVPo6TzR6AW6eqCpI9DD0AtIKuWTLzmup08p9RYOKJrcxZNt/juXBEyhVhemvqZo+Roq2w4eXwbTX4X+nwqP14Nl2MO5+9SU/5pLU7btlRfYlJ4LFMEOTaGWWTWzk2CuU4z6FWlfZUtnbLz6uKdf2bhU/YRxr/e+WY1glAB44evx1NKyZzSuDe+jOxxaimtkBvrujD0fVyuaXZdv5YcGGA2YugHN8swlY+GgaigKu8v9iOTczRIZ+p5iggRhJ6CCZ3rwC7f7CqLV9Ee4DJPofYo8/C5+mO74hv5gV2/Zx77nm5s8/tu7j9o/nKV+ZQaCUSRgxbZ3tXEMGk9ztH89NENh2JsRisvll/9HKZ2JW2UErrEuT0CKMDvxAVvk7N6aLE4empyy/BZ5AqQpEwkpgWLE/X5W83rZI2XKLtisB9E4/Vfl3r6aVa+1W8b+FX5mrzAgHE8txmLFuimZsF1pHzDyXQi2vs/2zeWNID4Zd0Q2/MeVZSwqx/gKYHD6W5/acwcb8YoZN0PdGiS2e1/ZuRePa2fx9zDwe/uBbvg/co6v429Rnnz3dx7couXkZkufsorzqVM/g/y4zFO8uZ820cESydU8Je6LO8+Na1CEnM9ZbJr48hKVgVPhMpoY7Jyi1QsBv4WP4JNw3Yfy3f1vNaxOtiyX+9Mc2Zq/fnaBpvfrLWqavse9SYdQ+Fm7amxBc4eSTWrm9kAUbC8xPahfeSBK1voy9YfxZqSVJppvOg+CUv1foU3g+lKrAvi3m5UCc2L0WXuupdxgWrIv/nVEdmnSHdb8l3rv4C9ud/gG0zu8e1ynB5oYUFrrWvm3k1txDtKGnJRKfczn6KPmRGqykGZ+H+/BFuA+hyRv5fMEOtuwpAc1vPLYQvfrLKsYu2ExefiHjMp+jtU8fmiplmkP5A9mgWX/sTF61q2fSsqGhYKQLDUVKyYpthewqKqV1/Rwa16pGOCJ5c/Jq3v99Hdv3qQn0aVeff/XvyM2nteHFn1bqluYIPsrI4Ibgv7guMp4r/ZNoLPLZLOsxJtyXD8L9TRfvqaudWxeNW7yVE3vpNa/vFp+qmE4AACAASURBVG8HbCoYYy4sjELQLlosxjcLNtOtuYkZSBc2nJF8E7EYgWxokHyQSlo571mVd1bBeSieQKkKBCx6dbvBLvqkbB9Mtaj0usShyGOMdprS6CfepNrNrvzR+vpJT8Np95jkiWQkNhsyI38NtLDPUJ4TPpoT/CsTjpst9u+Ez+f18CDdsS17Eh2s2oVoQ34xfX0Lae/blHCd0+/RTf5JjAbspnZIb5sP2iyA1TP9ic5UC8E9a10+H01fz6JNe9i2t5TCUvU9EUC/zo3ICvj4dqHehPnbyp3MWjeVj2/uRTAcYf9vcdtWbGEuJZO3wgN5KzzQ7ct0pCQYThCMZdJ54TPzj2hDvJ38UTH2WTn+te+t6/43JgQyXSRJVjBHda1wYQKeyatqkFNPhXFWJm4iS6obyl0HMlUflUGvQcPO5vdMegK+uSPR7+EyqqQsK/GHN3WV3tE+Knwm+2Wij0YIGBU6Q3es1OWiEjFkZncVySeA7ZC1+Dh8pqtrT/MtYFLWXdQp1UdAPRwYQRaJYc0ADWpkJb6PJk75135ZxeVvTOOr+ZtZvaPogDAB5UuasHRbgjCJURKM8PQPy/hn/44c3zxea8toSgLIdAgJdkvjWtUSS6/YZMrHMIuI02bYO/qjonRsnBj1B+iFnF33UDO0mn0g2zxS04lqLtoXuKUiS7po8ARKVaHrlZjXn60gjjrW/nwgG67/VrVQ1eIPqE5wds78eR/CDkPzKBc7vC2ROhTX7aQ7NmrmBq5+Rx8inCcb8kOkp+5YgazOQ8HruT80VHfcyYYew7hgFpFcWOWySHOuLnsgIUQ1xslH1yMzGgZdh728nvESOSLRhNLTv4J/BMy7YQYjkUSHr2FnP2/Dbp4db9K4KwlmrM1n7c5CAj6thqJ/f+4/tyOPXtjFeGtKPPfjck57Xm+WTSUPBfTzjJ2vm2MdIJIV8DGgi4VpTSuswxotpr5DjS+jkA9kWfe2b9ff/LdRv52LWmJR3JiXPYFyBLFtCYz7N6b5INm1qRBBc8JQ+5DTc5+Bhp3Mz+1cATuW2Y//x1j9Yxcayqfh04m2AmH1jkLuGDWX+75YxLm+Gbrrhvq/J2xwXo8Kn8XI8DlJRfloMS5gP0ZOsCzlAZAn63Fz2T/4V/BmLi/9DwPKnmKltK7yfOnxzejXWbVhvdT/GzWEdV7DYP9EMqPlY/p1akjNbPUaikrDjiavj2fYVYJ2z1nP/8rSzXFntdEXUScnk6t6tuD4Fi77yDhQZLCGuhMoiZ+tdp4xgXJ971b0aGEeKlsainDZG9NYu7MIKSXzNuxm3OItLN60R9/9MazRGm/4Dm6fDQ1Mfh/Cp0LntRsuf5a1htK4G9xhUqyyzZmQpdmcmOVixTjqWGhzhvV5cG47kSY8H0pVYNJT1pFLJQUqFHfRJ+l9zoYd4eI34AuTApE9/6xvtGTEWHDS9BpDCW+zXZjw60wJq2RT+kYizFmfz5+Gz6S4LMwd/i+4K0O/Yz8nMIf9Uj+eVUKgew1Fv4BtlI34IHwONwTGm17/THAwEyInuhob4I1Jq2jbUC0QHX3W9a0AaolijhL5bJCNuPLEFvyxZR97S0IUlYYSBEoYH5OXbefHpdsoC0WYvd7ZCe6GiITSshCxtBdjwc2HvlrM5BU7KAu7MwW1b1iDFdutvzdGAeLGmR40MYtp74uZvIrKwtxzTgeufmdGwvUAmwr2c927M8kK+FipmeNf6q/k3wcG1kg84VMaxC2TYOEYWPy56lTZ6BhlIm7cDcZq2msLAQs/NX8RkRDUMmnEZtw41GquGnBtMumyWqc1XPYuLBhlnegcLEdiZhJ4AuVgEwnD8u/trykrUslXb2vs8+0HqHLy5aHrFTDu3ni57qyacO3X0LSH/X312ysV3q7vgzE3xkyg3L0Mnmt34GEIP6XBCP/6fCHFZWFaiS3clfGZqbO9mtD7GUIWdbMcOx5GMfMRPBr6E9tlHYYGvj+QyLgq0oRnQ1cw3mByc2Ll9iJWblfv117pbE/PlOr11cgKkJOlhGVxWTjB5HXt+3P4fe3ehPvTgU+jMRsX/JJQhG8s/DBGjqqVfSAs2QpjywEnDaWXbymPBfTVCt7MGEZA0wcnlqeyu7iMvN32O/QN+YkbunX5pRgr3QBx/0hGNhx/nfpnx4px1nkoVkE1a3+FLQvij3euUE24rv40KsQ0myx/pvqBND/Jeg6plNVPAc/kVR7y16qeJM+2g6daqLyONb8mN0Yk5FwnKLQ/XrAuxoCnoM89zpnSnQfZn9eWAM9t7CxMQKnidhpMzWaqYZAWE5PXPz5bonscws+Y2RsOFAy82K/Cms2CU4ymqBrVzU1qZcbMcgvMFjCJj/+FL6R36aucXfoMfUufp1/Zs0kLEyNjwyc7XnNjVDPKzQ6Qk6WE4r6SIHtK9S986lqLHIo0kKmp2lyeZgTX9m7Btn32IbehJDSUrmI1H2Q8RVufXqD198+mmy+e8xIzec1at5s5G5JPLDTbZADJJ5PaJTVadXfUCpMYm+eqqtkteuuPx5Iy7fyU5SkdkwSHv0CRElb/AmP/Bp9cC5OfVa0+y8vWxfDW6TDnfZVoWLJHhdOOuBDmfOB+nEAWNHJwkDc9IfFYOAhnPQQn32F935DPod9/7cfWCiRjC1g7+j0CHUw6/eU2gWs+SRQgJl/2Hw3VYssI8NmceKhuA6wXS6OQ2VlsHiRg5ri1GNHyTJAAK2Uz1snGtte5Zb5sy9pII9trBvl/x0eEnEw/e4rVwr63JES3R/Uh20afUXqQ3OD/gT8Hvj1wpA6FPBQYSYDkiyQWljibxZLRUO4MfEGWMJ9HrsY3FRtj7c6ieJn8JLD87iRTpduJ7Uucr9GyZX5iCfrYb8uuffB2B59nmji8BUqoDD75E4y8COaOUA2pJj4GL3dX9aDKw6jB1qG33/8zudo/J//N+lxGdTjhhsTj4TIlLBeMsr534uPOJVC0P45kYu0zslWP+Rt+gF5/heNvgEGvw9/m6HqrH8BEQzH+YI1O1rXSPrHN7t74cbdf8fI1BEuW9dJeoOSIUqpTwhu/rmbNTruWsumf943+cTycMVJnVhQChgZ+4MmASXkgB3of7ZyDYfwuWAmUDEL09dl0XNTgxg9jh98qedZBoExZmcRvvzTJdsGQKFB8Gaot99RXre9Z8YNNzf70cXgLlN+egz++STwe3K+0lcIUexBMfk7147YiXKoy0d3S9Qo4/d+Jx4WAwaPNG2GFy2DDdCi00bY2z00M3014Dq1ASbICqRDQ8mQY8AQMfBGOu8Y6msVEWBkd5sZF5ctwH0pM8k3MsBIc5V1UKoq10r4awHZZmyKyGTM7bhJpLbbweaa+dcD4zHs5TiQmeaZKFmXcETD/7koJlwcm01q4850A+AU0qV2NlnXt/UZG85LV5+Yjgt+iXpsRN5Fi5igN7YWM18xP25iZ3/ltDUOGmzv/TUklP8VoSfBnqJ4qdpuL4l2wfan1+TTh6h0XQtzp5liVIhyybwQVLHZXPddIJGyvWsZYNwV+fwnmf+ycRCgEnHGfEh5achpCm9OVANxqqBEVDjqH7gJsMfQ2N0ZfaW1HKRR0dI2JQEmou2QIBd5JLe4K3mpa32p7RB8GaqWhuF1UKjEDCICPw2cRMckGj20iR4XPRPvzrMceRmf+H8f79MKjgy+PDzOf4GiRmNWfCj18K6ktzHfNsa/Kmb55rscLS/j35wsZcIyTtql/L6w+t1IyWRRp5e65kxAo2me/1v8jD2eMpIZJnpC62HzcdTuLePy7P0zPWSoHdY92PccDNDH4Of0ZKhrUif0V52+L4fYdNwtjuD6N80g/hVtVhzI7trmwXxbnK1Xy0+vhq9tgzgh3ms0fX8OE/6gwvuc7KZObGfsLYNNcpcYaM2PDQRj/ADx7NLxxquFcmbtyDhMf1T9+oQusmRR/rBUo6eqRkL8W3r9Af2zjdN3DMunHuIiY2ay/j/RiQNlTfBA6m/mRo5kS7sIDwRsZGe6nuy6IHx8RbvXr81/+5R9DF7EuYdyGufrXKirZ5LVSNuPhUOLPSghVxPK10CDdjIb4f6KRMF8QckQpf/Z/a3ouGXwCMl34SLSRVG6YtW43wXDcfNS0TjXHxlt2wuDNkHXZlzLN5iMZ7TT2XmcS5I7Al/YXW+z0P5+bZ/otWh9paF31JLuWxQkLOg+CegYh5M+0rlxxAKFCnSsY25hKIcRg4GqgtRBC+0vNBezLrh5ssnKxbB4VI7um/RgbZ8HHl+ujNOZ/lPxcgkUqKCC3CbSLLoQle2H8/bDwk3jRuXqGD7xkN0yzsIuumwKn3Km+TGHzUh1AYkb7/t2qf/VtM6BOS0MCVjnqFcUozlfCZK8heqU0sU+IESstY7VsysMhvR/pZsMCGpY+nsl4i8v8k3XHu/nX8Invv1xZ9hCLZZv4c0UqV4A0qJFJMCIpKI5HTo0Mn8O8SFv+5P+Jdr48dstcvg6fzHeRXglO6jP89lrBmf55pOAv1xGRsEC2oVRmkCUSw3xj4duzIskXOtxVFP+OvnHN8bwwYQUTl9ttzKx1xm8jvWkS3Mk/A5+QIeLCbZuszbJIC073K608FZPXsWJNQq+bBFZNgMaJvVo2F5hHUr0WGkSmCHGR/3e61Q2T0bBdPOTfKsKz5y3KbB7bFPsz4bgh0P9JVU9Piy8DOl4ANY6yNoHXbAo1Gtq/rjTgFKQ/FdgC1Aee1xzfByw0vaOqkF0L2p1tX8jwmEutz5UVwejB6e1jMOUFJVDCIRVibNi1s8tgC7dzos39APr+WyVR5c1Kbh7B/TB7uOr4qC0pkQ6BMvvdRGFigpnPw6lboRbj7rOp2Mll/smmOSs5opT7AqO4JvgA1TP9FJeFyS8q01UbrggN5eWrulMtM0C9Gpl0b1absJTM21DA0s17ePHnlRQUB1ks23Bv6BbHsQIO1ZUzyitNohSQy6jwGVwf+FH3Xsb+nhHpyFyZ/E43OxD/bDMCglPa1nMQKPa8FR7Il+E+nOefQV2xj5WRpvwYOYGnM946cE0qAsXpfVYDm7/XTeuYZ7MXUY1Pwr0YwznMu/UcMkQZPBH1oVkJlE4XwjmPqd92qBQad1c1/0Af6g8qbDgWJDP8LPN1o0F759eVBmwFipRyPbAe6G13XZWl339hw7SE3TGghIldQcYlX5p2BSwXG6aqvgrLvk0UJsmyb4tqF1y9fmr3b4zWx9J+odNh8lphnlluJISfG/w/6I79KzCaR0PXssEhAgoSF4vjovkHVjkrp/iXUC+4h8b1m7N4c+IONN0+lFPa1mNgtyYIzYR8CHq2rkvP1nUZ0qslH0xdx/9Z2NyNzIx05BjfOsvz0yNOJg/3PB4aQk1RzCX+eC+cmDC5tezvJPtudW1Wi9zs+FKT4fdxXtfGrl+7FTuozQfh/rpj2mRM7aajUW7WgVwYn1DamBlLZEsKZbZtaZwlmcfSPhzBJ0S00OZm9paEaFa7munYsS6bA7s2oUZWAEIaoWUlUALZ6vfY6tTEc8Y8mNhGsNnxqh/S7rWaa6NVvsMuKn2nAbdO+UuEECuFEHuEEHuFEPuEcNILqwCNOsONP6rMbi1nPggXv2VfznnrYhdPYHz7HH5owq/iyH98wMXYLggWx7PVE4oGOiifMeGh/UKnwylvZ37TUI1SHs4YqTvWzz+PLzP/4yqKyChQmtawfu9jH3Pr3P9n77zDoyjzB/55t6UnQEgCJEDovfciiKCIqAh2VIrdwztFT0+94u8sd56np6co6in2epYTEbuiIooUpUvvhA5JSEi2vb8/ZjeZnZ2ZnU2DxPk8Dw/ZmXdm3i0z3/fbA3RroW+zVj/wrJLo1r99RnfO5snL+kUIEy0up4NxPcwjvdS8EBhLqdTXIANS8LT/bN19VcGHi1t8v2F0+T+5z3cZ//BdwqTy/+Ni7585TAwzsYYEl4MHJvWM8KF4nA4ykmonAEQd6puZWqkxqEOXxxkVgwSOkcxLAaVlg95CPygF4+cGGfL3Lzjr399w/SvLmLeygG82HOC1H/UjP8PRjC3CGoz63gwa+KPMFnfa+1StsXhURSgT0iEhVfn7JEtsfBA4V0qZIaVMl1KmSSnj+2WdKDLbR/omXEkw4jaly+Hqd+GX+VCuU2PIkrNMqx7HMJs07wXPnW6cHRsPDpfStMcbKqng1nSES45R+rrTeOV/9Q863rBhPVrHzgIHSBI+3Rs2UxRzu+uN6B0atD6G5cfMixQWymRWFadWNOE7yxGpId6Y8gVtRXxNzrI0jv2/T+rBZzNH8Ny0AZYemI2TrZsYd8gcrvLdxgEZ+bsslknc5LuR5bLmTRqbZS7PBsYzO3Auy2VHnBZaxzZJ8dAkufK9X9Avj64t0vEGKr9st9MRYQKzglWdKMVdOVI4K69R6q38nX+7yTxY52H/hbzqH617NyvahuDgMS/r90U/N/Q0n/CmWV9u4v2fd4c6QYbmaaahGKFdLKoFTMRxUvHVAuxeBvNvjy8/rgpYFSj7pJTV009PBGvnKlFN6z+s3Bb0KT3a/90L3p6u+Eke7qQ0olI/4bpPqtm5ONxKPZ5YZVaskpihFJUM+3jKNArjsf0Y3oZNO0Lvycrf6qJ3NeFDGXiNeWVUFUYL+NMdy0jGfEWl1VC+DvTQrZEV/kpfC4ymHA9vLdnFb5z/40nPYxHj8opXMC/xL3TViQg7u2dz7p3QjY9uimynnJUaKVAuHdiKDjkGvTV0SPLE91D9PtiNYeWPcYP3Jv7uu5SZ3hsY63iaecG6sUgHLQQy/Pa09rx9Q+WiItGtvEe1huJ2ChwOofp1SkY7lkWcZ5xjcURXTqMrX9CvssLzpQNbMbJ95cJCCn2BUmTUUCtEACd/9F/Flb7f6+wzf2SmUcqj7shAmofdsznToZiYn/o6lJQYFgpGfVbMNJQoH4qBQCkvrjy/DCrpDs+dASW1F09lVaAsFUK8KYS4NGT+miSEqOEnbg2zbSH8d6pSFkVN0K9Eaah/ot5j8OmfYIkqCzirE7SyttqOSUYrGH6zUpG0pig9pESAHd4c2qB3y0nwpKpeC0UzmfpBpSqstq3GU3rFiCZt4dI3OKRRYP0WGiZVTEMEScO8mJ02h6WQVK7zzeSYjFzZCQGfBfryiP8CAJqzn1td+pVfk2Upr+S+S0565M38+KV9uGJIPh01wkKrYcg4MpEDQcnTX2+OPRDlARzGi5uPgoN4OnAOc+UIurTWSXqtJWK9u/bZqVzUvyVuZ+V3HRYkESavUMiwy6lEYd7nmsNznocjzjXb828edT+Jw2KrZ4DBbZtEjlfli5R441/I7ZXR/knD+l4oSZfPeh7iPOeiiO1ZQul/M8rxE+sKipSq0WGBEvTr29ZMNRQDHwpEWyq0HN4MC/9lPqYaWL3L04FS4AzgnNC/mjPa1gbf/NO8CZTRMX6VD+DiV2pm1X7lJzXbfS0evMfgsrdh2ny4ZS1c+lpk06xgDUd5AbQ9ldHBJ/id90Ye8l3IH3zXMD9oUglVwxGZyqGQrd5hcP8GNALKL518H+zGiPJH+bvvUj4IDOZ1/ygu997JNb5bK4pEnutYZJpp3eTgUia1jVw1hn0hTs1kFmyIDNoY9+9v2XIgdml/KSV/eGclf//IWn2lhy7sxaQ+uRGfRVZqAs9O7V9ROFKZp6XT1QrjezTn9WsGk5Lgisgx8fqjBUpY4LicDsY6lnK564uoZ6qUMMG5iPM1YeBa9hdVarLJHlekCVf14D3ujbPjIvraiFluy0jHCgY5ftGVDw4h+b3rLYSQyu8oPLdgQN+PEpcPRWUCMxNEYVbWcCsM9VTiGDdTSjldSjkduMXqBYQQTiHET0KIeaHXbYQQi0NO/jeFEJ7Q9oTQ602h/fmqc9wZ2r5eCDFW/0pqZPxVfwGO7YN9Kmd8Sia0PTX+82gJ+pScj6pgtWubKQLyh+mXcFGvO2tKoAB92jZjbnAoswITeTMwKqrqr66tucI8dVpFTsrozvqx80a1nw6TztOBc/it73fc6b+GhcEeqI0rmRZiSXIcFvq9oGgZan7ZW8wVz/2orEBN+HnnUd5eZs2PlpHk5txeLfjXxb157NLKlgDXj2zLqM7ZEe19WzauQhmPGuCUDk154rK+FT4lj45A8fojfSigPFQudX4JRAvD8OvJof1GqPNbUjyR/XUwMHlZRU8bMdNQRjuWK5c1iDTs5tjOhDYOxQwYFijHj8La6ETKQ/8eyW8fnM2Qv3/B1S8uZdFmle8jyoeium+trCpKT7zJq6eUsiJNV0p5BOhjMl7NTYDa//IP4BEpZQfgCBDu2XoVcERK2R54JDQOIURX4BKgG3Am8KQQsWq2VwPt8sJvXnbbEgGv4qyuSlhuTVzfakJTDQmUMl+AvYWRPhBtmZGdMjuqV4kQsCDQi8f8ldbUoe2b6jq4tStFbV2wc3u14PqR7bhzXGceuqhXxfYtJjW0JBAUTp5eFSkQrnphiZK3YoHdR4/zv5/Ny6DMXWHd+Z/icVZoSPmZlRE8R0P9RY6F/AGpGs2gfXYqA9vUjVac5I68HSMEisbk5XSICk1PAi2FeS5KrP1eVQhucoKxhlKqMnlN6G3NTKhtKgbmGorbpIJA+Dl/9bA8ZY7haMj9a+Cdq6PGZ5Zt44GSu0kp2sTn6/Yx+T+LeeG7UDiwmVPeIEcmAm3Uaw1iWUMRQlR4u4QQTbDQnEsIkQeMB54NvRbAaUC4O8yLwHmhvyeEXhPaPzo0fgLwhpSyXEq5FdgExGhIIfTjt2OR1CS6Um66Tje1ePngZpg9rGrCobqJlTk9YvePD1NDpVdeW7yDdXsjc3+0K7tDpDPa+09m+89hQaAXHwQGc613Jlf6bqNc1dWocbKHOdMGkKQJ0dWaI9Qai0PAX87pyh3jOnPdyHaM6ZJT4YeYGxhKsdQPGhDAB/5BFPhSI7Z/8ct+pj//Y5RGYsT3m81XgEXHrdvzu7ao9EU1UkVPHS7x8vWGA6zfp3zOQSnZr+o54nEKbh8bmdGe2yjyfXdrYT2AwAytKVDtQ9GavNT+IFByScw4IM33H1U17lI0FLUPpfI3UVIeqLj+A5Ois9z1iNfktUSaVxAoT25O9y7dlGrkFnq8p4hyrle1EPjrB2vZebjUXKBYCfoZcFXsMVXEqkB5GFgkhLhXCHEPSgb9gxaOexS4ncr42kzgqJQy/K53AeEndi6wEyC0vzA0vmK7zjEVCCGuFUIsFUIsPXDgAJxyK3Gnqw39bbRTq8/l8Z0DoquRbl+ohCnHQk9DsFL0zQhPilIF2KpxvYY0lHd/ijbnaAWKHyc7ZQ7/8F/KNN8f+K3vd3waHBB1w7qdDvq1bsxnM0eSkeRSnc9YoAQlfKjqJpiR5GZoO8XBWkwyN/p+p1vFeF2wFf/n028ctmJXIV/+Yi2zW/uA1dK5mfUH+ZQh+RV/p6tyZeatLGDqnB8rOiGWegMRXRHXFhRz9QuR7WIzUyO/35euHKRrUnTFmL8Wh2a8yyEqfnJaDUUtbADeCSiRc1pRHTYUvB0YYXptteao1VCE2ofiU7Z7nA6SPM6oW+Kyga2izh2vyWteYDC7dBz5YRJGzoSj25VqEhYZpSrEKYFLnvmBJTs1idrhqC8pY9cv7HER9L/S8vXjxZJAkVK+BJwP7AMOAJOklC+bHSOEOBvYL6VUxwPqfRsyxj6zY9RzfEZK2V9K2T8rKwvajYLzn1WSe7RoG1oJBwyfCcNujh6bP0zp5xwPRqGAsbCYFBgTpwd6TYZrvoI8neZcZsdVk5JyPzsORUdoaRtB+XUqCOsRXtHmNUlm7o3DGZivmHG0pVu0PpX756+LeNhcMbjSh/V1sBdjvP/kKf/ZLA525utAT+7wXc153ns4YpK49+jnGyzNeWTHLNP95/fLq2jra8btZ3ZiROhcq3YVcuaj31bsi9VSF+BoWeSYlbsiq15f8swPrN8bXUUi3jpnWgEkhMATEhwVPpRQHopHJVCkhHcDp/BFoE/UTR7OzH9FUwRUi1prjPKh6HRWrIgw08z5s3X7osYGdSITzcKGy0hgivcONgcjzaoBKSjqf6NSn2vDJ8TTw0ZbiHP30ePMeD2y6tW+0iAvf7mc3Y+Mgj2RNd+KReXiJTjiDpj0TPwdJ+PAcnqwlHItEE9B/WHAuUKIs1CqJqWjaCyNhBCukBaSB4QNyruAlsAuIYQLyAAOq7aHUR9jTo8LlLa2L2g6C14wB54fW9lLffAMpQOhEY1bQ2H8Hd9OGOMfNm/Ra0Q1TV4/7TjC1S8u1Y3z19NQrKBe0bbOTOHN6wbT8/8+JeDT+lAiz+f1B5m/qoDLQ4JkZKcsMlM8FU7cXTKbB/yTLc0hzBqdki1aOuakMr6neQZ8kxQPT13ej+teXhblLM5OS+Dsns25ZGCrijDlQ8fKmTJnMUdKa7Z8xsb91oIPYuHU0YA9Tgfl/mCFZhIWLFoNJYCT63wzeanzKvodmovv0Hb2yca8FTiVFwJjI8yfsTCL8gpTEWHmcOALVI7dr9OiWM+8pedXUbNFtuB07z8Z4VhBd7GNYyTxSWAAbww5n3QhLJm61OiV1CnXLMaueWUFt7veINe5Jqr+WhqVC4aX1vqZPELicdVeOGD89SYsIqW8E7gTQAhxKvB7KeVlQoj/AhcAb6CUxX8/dMjc0OvvQ/u/lFLKUJXj14QQ/wJaAB2AHy1PJKhzE/pKwZ2KIq+o7MlshBVH18lCWgvzopdmVKP0SuFxH1e+sMTwoVcTAgWU1e+w9k0pXad5MOloPEdUGorb6eChi3px1QtLDOs4NUnxWHa+63FKh6Y8fGEvEixkgZ/SIYsFt53Kf5fuYvXuQlITXIzv2ZwRHbKiTEhvLNlZ48KkJtHOF0KaQLmODyX0MDt0rBx/UNnmx8Vr4iyaXXoTpz1sHiZshNsplGuq0E6bRgAAIABJREFUNBS92B2JErbtcgpQfaTZaQlRQkXX5KXTwyZqDA4WBPuwQBW3NGfhVrYcLCG3MJEHLLwfUHK39ErqaLWk1mIvwzXCBJS/1dv2Fuziqa8387vRtVfGvtYEigl/AN4QQtwH/ASEu2A9B7wshNiE8qS/BEBKuUYI8RaKduQHZkgZh01Jr6zK+o8qS8ZDZO6JHjVliqptGrWCyW9F1vOJh2qUXvnfT7tNH3pmPg8ztE5cgOEdMvloXezztcuOdK6P6pTN+zOGced7q1izp6jCTt8+O5U/ntWFgsIy7npvVdR5YpHocjDvt8NpH0eWPEB2WiIzRrWPOe6HLSd3pwhdDSVkWiqPcso7+GnHEaY9vwSfqhzLvJUFfLLGpPtoDJI94URBfR9KmAPF5ZzxyDdRouKCfnk8uSAy0VRPQ6lqF8gXv98e+qsVl3ja0tuxxXR8kUziTt81uiV1tBGNnYTis9Rzlaq3XeOaz/PfJRMc8QAOdw31PtJQJwJFSrkAWBD6ews6UVpSyjLgQoPj7wfur9LFtR0KAb7WrBF85lnZulrOycawm5Wil9Up8FiNY1fsMg8e0JoKrJaqd+s0YkpLdEfV8tKeLyc9gTFdoqsW98hrxLzfnkIwKNlbVIbLKchKTUAIgZSSI6VeHvtiY8WDECAlwVkRJaRHmT9IftMqCnELaLW0kw2njtAPC5SwICkLOcUdQnDdy8t0fUBqARNxfoegUZI7Iuck+tgga/cU0VUV5VXs1U9s3rj/WNTD94ZT27Fg/QHWFlSaNfWd8tX9LgTXem/l64SZJIno9/NjoBOvBkbzWbA/pegnKWqFWjnW7ttMUcTvg3PwvboFx+X/rZnKGBpO7l9qTeC1YCfe9q35/joq/VwtOp5Z/WrB1fChJMeoS6W9OdUCwax7n0fnYZqW6IrKlFefLz3RxezL+0XkQ2hxOAQtGiWRnZZYkechhGDGqPb8cOdoHrqwF/dM6Ma7vxnK+X3zDM8DiobjqsWH/qhO5k7+usIo+EtPQwkLwZLyAJc9+wP7ihSLwKb9x3T9FWZIKfn69lH8bWIPJg9qpdtOuNQbYPxj31JUXBl4cOCw8SJHnW7mEMoiZdbkyNS6eMOGrbKfxmyQ+ukIS2Un3g8ONxQmEK2NLwz0ICCFafukMFKCe9sCpZ9SLfArECj6/bEjOLQJjpo43cMCpaqmJDWe1NhjqkKSeby+JbRF5+LgzG7mjmjtjTi6ay6PXtybJyb3ZfFdow2P01udKxpK5PYhHXIY3r4pM8d05PNbR9K3lXn1YTMap3i4oF8eU4bk07dVY64Y3Fr3oRlm6tD8Kl/LCpP65tHGogaU4HIoPTcs0j03ncbJsb/37LQEQ9/TMZ3KAOGFwL6iMr7bVD2TXVAqiZuTB7XibxN7RIRPh0mnhDnuf5BeWnkfjy7/gvtdz+GM0bI4/Btrm5VKn1aV91G8YcPxoNWww+g1nosmcg67acpLgTMsZQdUjPnpFQvXiZ8T4UOpW6xoKABbv4E+l+nvCwsUo94FVsnI02/2VRMk1oBAiRWcYMKw9pmc1jnbMFdD+yxKSUrkvD6xk0b1fChpia4oAfXMtCE1089Fhw45aTx4QU/+8M7KqJDaSwa01M1hqElSEly8ds0gfv/WCr6LkTTZIzeDlAQXX28wbw7ncggm9M7lngndcAjB/FUFbDtUQlqii+O+AF/9coDiMh/dWmTQMSeVhz41DplesTNaEwhrhzXdB1NKyRfron9js9yPMcK5KsoxfZnrC0pI5G9+g3ubSC34T+O7cMHs7xXnva6GUlMCRV9w6AWXxMKHk3v9V1BMEtOdH5Nm0hysguLYPYeqQsMXKPFU+D22H9a+r2SnZ3WCjuMUO2Mg9AVVt0lNTfRBMWL3ckgfH98xWh25GqY9IQRPXtaX37yyXLe1a9TNaVF4aTWUTfuLuevdVVErub3Ffpo1qh2BAkruyID8JryxZAebDxwjMzWBiX1y6d+6sWkjrZqieUYSr14zmA5/nG/oawClXPzOI+Y+wdQEJ1/9flREP5fz+0Wa9W4aXekMnvHactPzbTtUgi8QjPiuzMyN1eG4LxDlS+kutugKk/DfVzg/Y5Z/AkXoWwfUfrp+rZvQPTeDVbsLa83kBWYaSvwCxY+ywPqX/yKe8p9LD7GVP7lfpodJh08a1c4iqOELlK0x/CNhDm+Bf3WNdMCn58LFL+tHip1svD0dfvM9ZLazNv7wVvjvtMhtL50HZ/4d+k+PeXhRmY+XFm1j3soCjpX76do8nXE9mvHtJv2VcdTKLmRe8weCvG7Q6Q4iH0y7jpRy0VM/cLjUS1cReWNf/J8fmHvj8FrrBAjQKjOZ28+siWKdVWPWlxtNhQnA2G45fLR6L1sOGJt68xonRzUHM+NYjP4hQUmFQJFS8tHqvfxSUDsNXRNcThJcjoigiSEOJT3OSK4nCS99HJv5OlhZ083pEBVJkVotOPzZGJm8lKL7sUlPdBn2XjFq51CVKDKv6jFeSiKLZRee8Z/N455ZUUK2gr5T476OFRq2D8VbCgcslAh3J8O3D0VHcxXthpcnWtdM3Cem2iughEF/cY+1sd4SeGmC0o5Yjf84zLtZ0dJMOHisnIlPfMdDn27gl73F7DpynE/X7mPmmysMH3jRAsVFICiZ8dpy/vz+GsNrqVe9z3yzhcOlyupUe+NtP1TKa4uNBVN954cth0zNTgAdslM5v18eF/Y3DyK4sH98lR+655o3Z22U5K4oEPnwpxv4zavLYzaxAiXvB5QHXrcW6ZaqBzgdglM7RZaLsaI1qH8vHbJTaavySWm14HCQiN55nU4XT0zuQ4/c2B1dzT6DmtVQoo+ZFxzM24ER+sKk28TKBns1TMPVUA5uhPeus1YszczUU1ZovC+McML0+fDf6bFDkGuTdR8oZjtNdeEDxeV8vGYvRcd9dMxJ47SS+TiPbjc4CfDNQ9B1AkVlPt5ZtosfthxSbuSO2ZzbuwUPfPQLm01WwHpECRSni3kr9/DJmuiSF2rUq0d1noLeSu6TNXu54VSLGlo94+UfTL4vlCi7168dTLLHxbm9cvlk9T4+1snrGNouk8sHx2fuuHRgK579dmuEVqCmd6tGCCFYu6eIWV9tsnROj8vBuzcMJTs9AadDkOByctwb4NO1e9lfVM73Ww4Z+uOmDmkd8VtYEOzFn3nFcDVeJJNYFlSS+S7sl8eDF/Rk0uzKJljaSMJwl0k9DaU8AG8t28XcG4exYd8xNu0v5uY3f46pOWoxEhxV0VD0jpE4uM13LV8GevM393M0EiUEpODY+CfI6H9JrZVfaZgCJeCFOWfGLpQWprp5JlIqfQ1qqFpv1ecRUMIBR9xWsemprzfz8KfrI37wz6W8jXFcFbB3JZu272DyKxsiQjznr9rL7K83sftI/L6kVDQlJ9bNY9XG1ijFD4xRrx7LfJUPNL0VXjjXoSGyOUaZlFJvgEPHvDRNVR7Qsyb34a2lu3hjyQ52Hi4lJz2RC/u35PLBrSxl8qvJa5zME5P78tvXf6oosqimT0slIOSd5dZ8hMkeJ/++pE9U7k6Sx8mE3kqgxoFj5YYCJVvTUXOzzOUjMZxxLNQt/veU/1yOh8JwOzVLQwgRUctLq6GEM/j1NJQgggXrlSrPnZuls2FfcdzCBIwFRywNJZcD3O+OLC75pude/ui7kk0yUjOVOJgfHMzFwQWMdK4kiIMD+RPIqMVaXg3T5HVsv3VhUiME4a0ptVp0zTI7l1T8+d5Pu3jgo1+ifvAlFpoN3fLflbr5AlsPllZUkLVKe7GLS5xfRW48spW7DtzGxdrtGtQ3e++WlZFsejekOuSzoaFtN6zHnIVbK/52OR1MHtSKuTcO56e/nMHHN4/gquFt4hYmYcZ0zeHbP4zijnGd6ZkXae5xqUKEY9G9RTqL7jiN07tGJ51GnNOk4rHTEf3dl5z5b+g7FaEq7R5wp/CQ70KeDJxbsS090R06f+U53JraVuH2AnoaSvh3t2Sb0lbCaksDLcYaSuX2RE1gQyOKeTPhXk51rojYPsjxC2967iUXff9lOOnXLQLkZljo6FgNGqZAqU7J96oSKLdmHrOCqIbiGCq/L6VktqaURJhvg+b9UYoze7LyYM1FLt3vnkOyTlawA8k9rudpgrEDV10K/ppT2lb8rV09uhyCaUPb1MBsT04mWgixXrLtcK3OoWlqAtePbBeV6OkI2ZlaNYntQ+zZshGNLAhHM4Gity81JRXOfQxmrlVad1/6JtumLmVWYCJqnSU1lMPichprKIkVvXdEVO2ucMWHhNAxA9o0MUz4NMNYQ6ncfse4zkwflk9yaD5XOD8jT+gvlDNFMVe75uvuU5dqSXKqFoMlB2H9x7Dx8xoLPGqYAiXeXvKWifHLibOSqCGyGsUouyirsaLjfjbs0/+RzA0MjSqxreb73Jrrl9BS7DPssw2QIPyc61ykv1PD8A5Nufe87ridIipT/vFL+9Apjj4j9Y3z+uSaVhSAuivRoi0GGb7sRf1bxny4JlrUkMwqD+gVo6yI7kvLgS7nQKczcSZGO87TwgJFbfLSaDxqU5z2wR+QDlwOwchQ9YLcRkmW8qm0BA0Wjeo8lPymKdx9TjfuHK9UHD7DuVT3mDBjnUt0t6sFyk/b9im1Cz/8PfyrC7x+Mbx6PjzcWfGdVpOGKVDctVRXKVa+gbaTWl3Tom+FQNGrrxSmHA+Xe+/ix6AmBDapCUx8msJW5j0o4iEHxTRg9tHlCOtdKa8Y3Jrv/nAaM0ZHdscb18M8U7++43E5uKCfefTW6C4WWz1XE23VgLCGkt80hb+e203vkAq05iXDa8SpoaQnRd97Lp17IFxFQC2wtBY0dei51uwVRDB1aD456ZWmo79N7KHbNK1DtnFVDCsaSlhTmtgnFwEkYO7rNdqvrnM37dlFrHxqKiz5T2TRW28xfHmv6fmt0DAFSmot1T5KiLECTrfWq7pW6HkJXPFeRcG31AQX/Voblx8pIJOLvH/h7f6vw8Sn4dI34ZZ10OsSzujaTPdmrAq7pfJdmNUZGjWoP2O6ZNMpJ40uFrSM7PRELh/SNua4hsb1I9vRyCDPpmmqh2m1XAImjFZ5UD/grxiSz3u/GVrhqAcY0bGyi6FebTY99CokVF5fR6AkRn8uRmV7tHN2aASkWovSFjXNa5LKXWd1iRzvdkaFYs+Z1p8dh40jPr1BozyUymt/vEqJZEtNcNEkxcOyoHkv+OXBjrq/D5+sFLbtxB56Hpxf4xUMwjRMgZKQDuMejG7FW10atTYucZLV5cQ55btNgklPR9XzuthCvsEybx70ugQ6nVnhf8lIdjO4TWaNTK2ATL4K9DLWUDwpdD59Os9OHcAnM0dw6xnmfbkrOBkCIOqYlk2Sef3awfRqGfk9D8hvzBvXDiE7vXYdrmG0D2DtA75Pq8ZcN7IyfFvdy96qWU7P8R5GT0NJ06nvpetr0cl10f42E9yV19ZqEvlZaVHvt6Tcz887I7XseSsKDMOswTh3Ru2sn7eyABlaiSUnOHkhMBafQWmWoBQsz50coTmFUZu8hjuU9gy1VduhYQoUgEHXwfWqLPkW/aBxfvXO2by3km+Sf0rlNocLul8ArYfC3vh7aVQbTwqceofurtFdsmP+cLJS9UOdY7WxjYc/+a7kqNRx2DrccN5ToLJ165Wr10U03J+uGV2ap/P+jGF8OnMEL145kM9vGcl/rx9KexPzSk2jfaDq+TTUJqiDxyIbnVnBTENZsD46mmn8Y9/y047Ih7peR8orX1jCuoIigiqV2UxDiXrwa353ewvLOOfxhXywIrI21ns/7zacP1jLQzlY4uXnUJ20RJeT9bIVN/p+xzEZKTTKpZs/Bq7hvPMuJllHYKqv5YlhNqsuDfuuVFcHzu0Lnmo6bZMyIKcbTJsHM9fAtQvg1g1K3sfS52IdbZkoddSo13tOD5gyV6k7pkNmagKjOpvb1ScalGY/p1cLUzt2POx3ZPOsX9OGudUQuO5r6HpuxGazB0kFwQBs+Chy2+5l1Zxl/aJjThojO2bVqSAJo/1d6Po0VCaoQ8cqw8+t1vgy+u0t33GEW978OWr77qNlTJnzI3uOKoExOw+Xcs2L0U7qtQXFTP7PDxw3CZ1XayhRocMaq8cf3lnJloPRSb6xSslbzUOZ9vwSdh89XpFs+UlwAEPLH+cu31XM9p/D3b6pDCl/nPzTr6dTszTdStPq0iyrZRtL86sqDVugqEvXJ6SCO8l4rBFqR7v6wZ6RBy36QEomLHu+6nPUQQCyeW/964JSa+v6hYoGltff9Fx/PrsrSW79r/mW0zsalkVvlpHI7WMtmp8McDoEH910CteNbEcpms++3zRFOGuIaWP3e+GNy+C96yO3/+c0+P6Jas3XxhraFb32NUQKFHVbZY9F35xR2PBTCzZjZEgqLvPzUqgz4rPfbqHYoCnakVIf21X+japqKDsOlcas6hwvWkFTeNzH8wu3qkKZoYgUXguM5h/+S3kxMJbDpDOknWKiTvFECxS1yWubbM43gR4V7YHD1JSA+fUIFE9K9QWKXr+QYBBW/Tf+8xqwMpDPeeX3UJ43THVdjRqb1RWa9YgddYaSj9I6U5OR7HYw+7K+MXtLXzeyHd1bmNdxMiOvcRJdmqfjdjp0a3npEdMk8v2saO0kzCd3QcEK/X02NYZWe9B1kqtMXoeqYPJyGfhQvo9Rvv/7zUqehl7FazUHVEm72tsorA2AnoZS+XrLwarnbjgMxKKeKWzBhgMxE1KlVO71cn+0EFWf042fvyXfzteBnlGVmRcEelqcvTENs/RKGHUvFE9qFYs3qj51vX4bW7+G0vgbCBnVHXo9eBqnOFeTsPy9yo3ahEkLDmkpJY98vpHHvtios1cwd8Ue5q0sYETHppzbK5ckg46LVUwEBiq1DZdT1JxAWRpDG1z6PJzzqNUp2lSBWE55gJ92VCYXF6sacFkVKEY++VhrqHArgUCMcihStSTXnjLS5KWZiOreCxe3rApOA4Gi1w8lGJQRGooeF8z+jjZZqbq5Z15VlNc53bOYNGEs589O46NjF5MsFMEalHCqc2U8b0GXhq2hlGsFShU0FLUQ0fNl7Pwx/nNifGP8xfUSt7reQpgVrLQgUOatLDAQJkpPiY9W7+XDVQX84Z1VjH/sWwoK9ZMy9xZVPVkz/KBxOkT0jWnQDMvUh+L3QmGMisKHrBUntKk6sTSUhz9dz/QX9JPsrK5PhEE4yYgYwSLh/YPamkcpqrP11QIyEJQRzv1oDaXyd9wjN4N2WcY5b6ahz4YaSvQjeXC7TBLc5ve8L4hhIrPa5HV+ryyapiYwZ9oAHMJYqFaVhi1QokxeVdBQ1NWK9R6CVSwI+UzjmZTI6BC/JGEhS95COPSzqrpOsdhysISZOo5OwDT0MRbhB43LIaLi+fXMh6t3F3L7O5GrpPvmra0s+uh0R0SE6ZJycvRfb8holQx1ouOy7Ud4/EtjoW5U8FGL0YLrN6e2N3TsZ6Z4uGJwawCuPqWNoR/G43KQ17hycRm+Vrk/wNUvLuFv8ytbXkQ5z1UCRQjBfef1MJzPDJPK14Yaisbkleh2cOWwNtV64KtNXs9/u5FAUNIuK5UE1bOmpnrENXCBEoeGkqDyFSRnUiGz1eXo9QRKp7Oit1lgevtSUqy06tQjRka+lJJVu+KrZ/bDlsOs3xvdnljP4WoVl0qgRJu8Im+ctXuKuOjp7yNMJaAIxmtfXkYwGLIR9rzE/KK9Lq3yfG2sYWbyesOkWRrAd5sORJib4qVri3RemDYgenvzdF6/dnBFc6xuLTJ46vJ+UeOapHh4+op+JKuc12Ez2awvN/GVJiQ52ikf+bsd0i6Td28Yyhldcyp+7/1bN+bZKf05v59xHphTxNZQstISmH1ZP15YtJX5q6resletoazZeZBXFyuBC6IWSlT9inwoKebahLpV8LCb4cv7lIKPavSc8lkdoc/l8NMrcU3NvVvfJGCJGCYvIQTJHhfHyuOrCfbTziNR9bCqEzpcYfJyOmKavB7+dD2lBqGc32w4wNcbDigh0CNvh02fKR02tXQ/HzqcXuX52ljDzOS164i5ibTwuB9fQOKJUYLFrK3y0PZNI17PGNWO35/RKeqYMZqKxuf0as4/L+hFotsZUZlZoJi69Bq0SSki7UE6+U/dczN4Zkp/gkGJpPLzMDIjg7FTPoCTZhkJ/PWcbozqnMOt/13BByv2GJ7HCuqwYTd+XvlhO1OG5FfrnEY0cA1FEzZsNRkuqZG+v8QoH+TsfytCyGM9J8BbncWBhfcxrnuzuE9757uruPrFpWxVxdX7asjkZeaUL/X6Y0blzFsZWqGlNIWrPoPBMyC5KSCgaSelMsKk/9Sc7m5jSFQtL5VAaRajPHpaostSrlE832Lvlo1NBVCY4e2bRkRwVVxLKKHN2l71oGPyMlnMORwiQriaLcaMTF44nDxyUR/Gdm/OloPHqi1MILL0iptA3M3x4qGBayhqH0ocAiXgBafOR7P9OyURz6NxxDldcPpflQTHfWuU6xxYp2gtR7YrprYjkT6NF/e05BrHckvTiWoaZGLyklLy886jtMpMJtnjNFz16x8Ln6/bx7cbD3DDqe2YNiSf0mo0rVILlChU2t5xbyBmHHyJWttKaQpn/k35Z9g026a20GbGq7/fC/vl8d5Pxlnip3XKtvTwNxuycV+kadao531QE6IYLvoYDMqIUOYjJV5SE5y4HAK/5hizsOFYaKsYN0p2c7TUh4MgzdBvNXDHkJSKnBKr/qZYaMOGGyd7CAZlrWgTDVdDkRKObKt87U62Ht679gN010g/vQxPj4QiA3tmQiq0GgQtB0DfKXDVp/D79crqWcPL3pEclEY5HiLS+afdbbBK2nWklPOeXMTEJxfx8KcbKPVWpaGo4oh/9PONDH3gi2olPFUIFKcgKLUmr0qh2DjZE1HvSY8eeQbOeFuY1DlRJi/VdzCkXWaFY1yPSwbGri+3r6iM95ZHCqXvNin5JbMXbOb0R76J2DfzrZ+Z9WVkROPq3YWc/sjXEdvufHcVc1fsZtLsRawtqDRx/7jtCFOfX8LoLtFNv2KVXjFDW/G7T8tGZFLIe56/MNC5XveYkUtnwPzbIBjEb7ETpNMhGNsthzO76VslIkxews/Ybtmc+e9vdMdWl4apofiOw9OnRNbWmjVAKdFshW0LMQxwPLQR5t4Il79jfT46zvx9NGGy94887n6cTo7K1qkHZQZpFz7JuwWNWbXgHVz4WRLszEcJd1aM+Xn3MXpHFjylzBfgiud+jDBXAYZZxVYo9VXPaRcu8Od0mCc2OhyC6cPyue/DdbrnSfY4uchCoUubuiEqU14lYIQQ3DOhG/3zG/PAR79QUBgZeBKrudaOQ6Vc8NSiqG6hlz27mKlDWvNiKBNey0OfbqBbbgajOmWzr6iMy59dzNHjkaH3R0p93PT6z7p39o9bDzO8fSaZKZ4I01es0itmaDWUjCQ3j7qfoJdDx/8XQgLix2egUWsGtjEPMElyO3h++kA6ZKeSkuBi+D++1B2ndso38sDi3UVKiHEt1BJtmBrKwfXRhRqtChNQGlxJE1PPps/1ncJG6AQD+HCxQbZkrPcfXFD+F27zXct0720MKX+c1WnDuPvrIl4LjOalwFjWycgV3yuLd0W1Hp23siBKmJxozKO8IoXslcPacMmAaKGRmuDiP1P6V0Tv2Jx4YuWhCCGY0DtXt5x+rMTGu+eu1m09DVSUVTHixUXbQuO2RQmTMGZr/oWbDjFrch8m9a1sZlYtDUXzuQxL28cpztWmWn/FET88yaDWGfQ1aWs9ZUg+g9tmkpmawMer90YU4VSjNnl1zkpk5a5CRLWWmsY0TA2ltiqfqTm4CZpY7MmhceYHpDrRT7BUdmZpoLLZ1bLth/GaOMP3l/hYtv0IA9s0qdi2aJN+a9ATSXglq+ShmEd5ORyCB87vyWWDWjN3xW4Kj/vo3CydSX1zLbWMtak7tE55I+dzmk6PErOuk/uLy3QrCYeJdVf/UqAsGhdurPq9sL+4nH9d1Jt/XtCLi576nuBe6055LVrfYcvjSn6LJStt0W7EsX08M6U/N7yyrKKHfZistAR+r6q1t3G/8YJZraEcLlYWnYnoC5/q0jAFSl2QHEe/EM3D02/ysQ9u2yRmuRM/Tgq1K7CT0JXgUvtQYuShhOmRl2HsL7E5KdCWRdEKmDB6XRTNNJT9ReXVavykd714CUeBOR0Ct0vndxuHhqINXhCeOBOr3Uk0TU7greuG8PCn65n11eaKXR2yUyM+y0ZJxosudemVI0WKQElGXwusLg3T5FXbZLZXKg1boeQgLH46YpPyQ4u+ddxOwR/P6kqnHPMy+0EcdMxRQpSllOw+ely3BemJprL0ip4PRb/0is3Jj5XikGDURdF45dM8I7FaeU9n91Q6pg7T5KlYJTXBFXGs0yFMM+Xj5WiLkeCyWP6p1VBIViwQQgg65Jjnh43v2Ryjj05t8nKhRMQlCVugnBw4PXDWQ8bV69QU71XKqv/8asRmt/TyWOZ7UcMvGdiKHnkZjOiYRX6m8WomIB3c9vZKXly0jbMfX8iwB77kb/N/OekCniLzULSmA1s5rq9YN3npaCgmJq/M1ARO14m0qjjWKQxrZ7XPTmXasHwApg7NN2yVbCavbhrdIaKfiEPolAyqhkBxpTSCU/9gbXCfyyNeajU7rTmtRaMkZo7RbxGsNnm5hJ90Srjc8Zm1ecSJLVDiocNYmP4RtBtlbfznf4Wj0Y5EKeHckrfpLiId+25VMcWnrzB2RAdw8OPWw9w9dw1r9lSGP9aF6ygews8d3VpeBsUhbU5+tKYcQw1F56Eeq9/NXyd0o7XOYsoh4MELevL29UO5bFArkkPVsZPcTi4d2Iq3rhtSoRHlpCfyytVQMEE5AAAgAElEQVSDaKsRPtlpCTw7tT9/Gt+FpqpOpTnpCdx3XneuPqVNxHjdYJJqCJQElxOG3czPrl6xBzfrEfFSK1D0WiT/dnQH/nVRryhrhTpsOItCPkm4nevdH8Yxc+vYy0SrZHeDy96yPt5XBqv1Q4vDD9rznd+y2l/p2P9i3X7+co7SdKpTszS+uHUkPf/v06jjq5ZdUvfsK1JCRuMpX29z8hOVKW/kQ9E1eZn/dnPSE5k7YzivLN7Ox6v3Uur106tlI6YPbVPhW7t/Yg/+ck5XCkt9pCe5dbPfu+dm8PnMkSzeepgdh0vITktkeIemuJ0OTuusREht2FeMQwg65qTi0pmXrskrDqe8lkS3A4Tg49RJ9D5q0rcnuWlUF1atqdCo8OWkvnlM6ptHSbmfAfd/Tqk3EGHyOt25jKaiSPfYmsC+q41IzIjoQ7LncCGT73sfR1IGZ/dswZXD8s2jj8oKo2uBacgWkZEb2w+XsrewjGYZiUgpWb2rUPc4o37UJxvhLP1YeSg29QsrLYAPl3j5+/zovKKCwuPkNTZ3Tmcku5kxqj0zRrU3HJPgcpKdbn4fOByCIe0yKzLP1XhcDrrnmgd/6LZdqIaGEhZ869IGseZwa7o5tusXehj2u6hUgyiTV4zyNSkJLpqlJ7LlYElE6ZWmoqhWi0vU2lJXCNFSCPGVEGKdEGKNEOKm0PYmQojPhBAbQ/83Dm0XQojHhBCbhBArhRB9VeeaGhq/UQgxtUoTsvQAE8i05uzqNA1feWRhtxb+Xbzqm0nw4CYe+2Ij5z3xHfuLTaoFJzWGBHNH+U4ZbS9eV1DI6z9u59SHFjD52cW6x0X9yE9SwmYJXR+KbfKqt8QyeZWU+5n8nx94V6cEy4VPfW9+35xEOB06FR6qZfJSjk1wu5nmvZ2lwY6RD3aHS6kJOPR3UcfG8qHocX6/PCDS5AW1W1yiNp9MfuBWKWUXYDAwQwjRFbgD+EJK2QH4IvQaYBzQIfTvWmA2KAIIuBsYBAwE7g4LIesIGPt3yO5qPKTTeI7fdYgpGS9QtO4r3DI6TjtXHOIx9yxAsu1QKQ989Ev0ecK4PND7csPdASl4M3Bq1PYbXl3One+uZvuh0uiD6hn5odbDLqeIjmmzNZR6i1lxSIDXf9zBLzqtEAAKCsv4zzdxJAWfQHQ16zgy5bWENZREt5MDNOYC791MLP8r9/iu4LXsW2DmGqUmoM4TX6uR6PlQtFw5rA39WjeuU4tGrQkUKWWBlHJ56O9iYB2QC0wAXgwNexE4L/T3BOAlqfAD0EgI0RwYC3wmpTwspTwCfAacGedsYMtXcPEr0EinzlCLPjBhFvd9uJaSzd/TNaSKRr8n6OnYSi+hxIPPW1lAqdekRPyouyA3uicDwN3+aWyVzaO2l+mUO0kickXXThgX36sNHpjUvUrHJak0lKjExpMtJM3GMtpnmXa1/NHqvabHf7iy6r096hKnMO/YGC9hDaWyna/gJ9mBOYFxLM2cAGnGFcK1wQxWNJQkj5NXrhrEjadXLqTLZO0u5OrEdiKEyAf6AIuBHCllAShCB8gODcsFdqoO2xXaZrRde41rhRBLhRBLdSex4WNIaw4zFsOEJ6HXZOhzhSJkrvqcQpHG28t20cGxO3Q+vfeh/B8e4/UHOVJq0qo3MR2mzafk9H9SIittouuCLXklYK1vRy+xiW8Tbo7YNsszi3+4njHsqVDT9G4Zp0IYIryS1V3p2dRbYjnljar/hjl4rLxaTbbqCoduuLu1R+aB4nJmvBpZTfw3ry5j/d5i3SCChBg946OivCy0AABFqFw1otLBX0gqtZkFXesCRQiRCrwD3CylNAsv0HuXUZXbVdsjN0j5jJSyv5Syv+7ZZRD8ZUop+T6XwcTZMGEWdDkHnC62HDhGuT/IURm7p8lhqfhGEt0OmsQoCyJdCcwpG8XPwUoHo/KlxiaNUuZ4/hkVlSElXOxawPXOuZbOUx0cAs6dtbBKx4Z/9Lrhlzb1lliJjd1yjapoK3gDkq9i9L85Gahq2HBJuZ9LnvmeDzVdFpduP8pFT3+vW1YpwWVultKavKxoKAAEgzCvckGaI44Cstbux1rVf4QQbhRh8qqU8t3Q5n1CiOZSyoKQSSv8y9oFqKsD5gF7QttP1WxfEPdkMlopjnIDwglNC4K9OCJTaSyO6Y47KNNZGFRixCf0yq0w62w9WMKLi7axZNshSsoDBIKS414/3oCkqMzPbFU/e6uaxXnOhWSKaFt0eEE43fUJTwfOiepDrUdWagJ/n9Sdx77cxEqD6DE9glJ5AFQFV0VeTf0JJLCJTZRTXqOhTBuaz7vLzc2yr/6wg9M6GycxngxUNVP+zSU7DZtYFR736d5/ZjXOINrkZamigJTwykTYsiBql6NaRW6Mqc0oLwE8B6yTUv5LtWsuEI7Umgq8r9o+JRTtNRgoDJnEPgHOEEI0Djnjzwhti4/BN5ja7dtnp9IxJ5VyPNztm6rrQwlIwd2+aXhx0zEnlT+MUwo6frPhAOP+/Q0vLNrGmj3FbDtUys4jxzlY4qMopP4XysokK6t1dMzKXANkiUKaC/MeL6d2bMoL0wfw4x9HM6ZrM3rlGVcvrQrTh+YzpG0mfVo14sphkYlhTiH4btNBJv9ncVRi42Gd7ng29YOoTHnN6rlnXqOIjHM9tpxklbH1cOr6/mIv3j5ZY+5D2nwgerGaoGMGU1OVKC9WvqkrTGqT2tRQhgFXAKuEED+Htt0FPAC8JYS4CtgBXBjaNx84C9gElALTAaSUh4UQ9wLhJuz3SCn1250Z8GXqeF79pR+nyK1M6punm3AlJbTOTGHDvmPMDQ5jTHA55zq/r9i/NNCBfwUuZFGwO9OG5nPrGR1JS3RT6vXzu9d/0nWmqylVNR9IsihQSqVxyfZwLPlxgzFOAa9ePZjBmhj8/vmNefkH8zLg8TCsQ1PuPrdbxes531V2ptxXXM7UOT/iD0qyNQ+hy59dzHszhsZU9W1OPswabIVp0ShR6blhQJOUk7+CtFNUzeRVFqPLqV5b7VgaSlWivLQ1BOuCWhMoUsqFGHt/RuuMl8AMg3PNAeZYvnhGHpvyRvHVdi//8w9lTVkbOHiQL9Yf5B8frefpK/oxomNWxCHPLdzKZ2v3VbzeoonAmuK7s0IonNOreUVp7o9X7zXsvaCmlMoHf7LFwmwfBQcyhc90E5GEgB+DnTiEfnLWfed1jxImAGd2b0arJsnsOFwzYclZqcYPhh+2HKpoqaq9MdcWFDF/VQET++TVyDxs6o7oBlvRYyb2yeMfHxuH1Z/XJyqu5qTDUUWTV++WjVhhYlbWW3oWHjfX2KukoRzQ7wpZmzRIw/beQBpjNl3I/b7LWCMjzTDHfQGueXEJu49WJi76A0GeXRhpXvJqwuvUAqE8pI0EgpIFFpyLjSimr6hsUZpJIX3FhpjHfR/syueBPhHCJGyK80oXD/ouNjx2XI/okGRQnH8vXTnQsMhevLRoZJz1rM6l0fOhfL725HfM2kQTnSkf/d1OHdqaXgZtCAa1acJF/U/+hYR+Y7jYGvWUofmmVZX1eG/5btPIN+35LPlQ4mmxUUM0SIFywKDjW5jygIzoQV1QWMa+oshjopOBKr/A8kCQj1cXMPSBL5i7wjymvhmH+MDzJ4Y611Zs84gAb3v+ysXOr2K8E8EM3038x38WxVLRjoSAn4PtuNx7J0tlZ8MjzUxJ+U1T+HTmSFo2tlhK24Qjpda0rajikCi9623qH9pnmd6zLdnj4tVrBjNjVDuyQ0VOW2QkcsvpHXnxyoH1wtSpGzZsQUNpl5XKk5f1I8Vj/T0eOOZl9oLNhvurpKH0uEB3c20GbP9q05XfXb6bm8d0YO7PBWw+EB1J5TP5aJZuO8wTXxl/+Wrudr9ES0d0FzqHkNzrmsOCQC/20UTnSIVyPNzvv5x/+S+gtdjPMZLYJbMMx4fxxLDJOh1Km9ZZX22K/SZMOPORb7n73G5M1Wn3mp2WUNHOVS9MsX9+1fJbbE4sQmPy0r4Ok5rg4raxnbltbGcCQVmtXicngupUGz69aw6L7hjN3BW72XaolKOlXt6JEfn24CfradM0Rde6oBUglvJQhv4W1n0AhzZGbBZAOW4S0Dbpq75+0SA1FCuU+4MM+tuX3D9/HW8s2RW130ygvLRom6VrNKGIMxxLDcvKe0SASU5rOR7HSeQX2cqSMHE5hKWbd8rQ1pZ7tRuttoLA3XPXsGhzdNvV4R0qmxVpNZTGyW4u6h/dQ96mYVLfhAmEnfJVr+WVkezmiiH5/Pnsrpzf15qJ7955awnotGzVCm1LGkpyE7jyExhwDV5npYk74EjgSwZGj28z0tIczfjVCpRYmAmU4nLzKI4w2eIoTiFNq4y0EDXfCz5WxEiY7LRE3rh2MD1jVF0FpXqpGc9/ty1qW99WjbltbCfdld4L0wfWi0gfm18vVTV56dE/vwlNTQJYwuwpLOOnHUdijtPzW+mSkgnjH+Lt0xfyfaCLsk0GkUGdZ1jZUWvnNMEWKDo04xDjHT9EbGsvorWYWOyXjQhIYdr4ao+sWrtSM2LFtKtpl5XKO78ZqttdT01ZDH/HKp2oFqdDMGNUe7674zSuGRFZirxXy5rNh7GxqWlcDkFQxu+U18PjcnDHuC6WxhaXm5eugdjl67UkuD0UowTQOKWP5kSb4dnzU1zn1MMWKBo6iR18mHAXI5yrIrZ/6LmLEQ6Tpjg6HCadz4P9DDUUn3RS1kXfcVYdrGooYdxOB5cN0imaWbFfkJ1qbhpLToi+0cJmjpz0RCbY4cE29Yyqhg0bcUG/PK4f2dZ0jEMQ1XFRj3hNiOv3FXOMyiCcHmKryeiqYwuUCCQPup/RLXeSIPw84n6SBOLL8P6rbwq7DLSQu/3TOH/UQNpnW6vtpcbs9xSvQAG4eUwHTukQPU+XQ/DwRb05p1cL0+PHx3Ik1oDDz8amLtFtXV2N8vUAt43tbBqyP657c5pnxI6+tFzLC5BS8v7PuyMSpV2idiIs7btcRVexnV6OLYYmqkxRzBjHcv2deuNTPJQkNePc8vt4zH8em4It2C0z+SgwgAvL/4K391S652YwfVh+3HPtZLKKqUpIZqLbyQvTB/LkZX0Z170Zw9s35erhbfjslpGc26sFU4e21u31DZDbKIlpOlFeTlug2NRjHFXMlDfD6RA8M6U/uY2ihUafVo3428QeOkfpncf6PDYfOMa+onJKVNU6aotfbdiwHrkhB7mZEz1P6NgeDTizezPun9iDH7ce5sGPW/Ov7RcB0DTVw5RT8ytanE4e2Iovf9nPF+usJfr1zMvgtE7ZrCvQb2IUqxS2EU6H4KwezTlLR9tolOzhreuGcO+8tXy8ei/+UBjo2G45/Gl8VzJ1TGL1MbLHxhwpJd9ujAwk+XbjAYa3b2oYPlxf0S0O6a1+DbJ2Wal8fstIPlixh8VbD+N2Ck7rnM3oLjmW75l4NJRwWahSaQuUOqVAKvkgZj2XC6T17NOMJKU8y8A2TXj7hqHsLy7juDdAi0ZJEYlKT329JUqYCOD8frl0bZHB7iOlbDlQQqLbyZndmzGue3Ne0dTj8rgcFWWxq2LyskJOeiKzJvelsNTH/uIystISaGRSvj+ixpNeVIlNvUJKyV/eXxNVC+6K535k2tB87j6na4MSKo6gl8GOtZEb598KhzfDmP+rsoMelD4lFw1oyUUDqhY6H89irW1WCkluJ8eCtkCpMdxOgS9GGfbVsg1rg63p6tAvnnhUpvBpUL8Dox5JmtyN7LToL/Tztft0ax5J4L3le7hyWFu6aqr4QrQWkpHkrqgQECupsbpkJLvJSI6jJ3x5Mfz8WuS2Xcsgz/pnaXPi+XBVgWFh0RcWbWNw2yac2V2/5E99ZOjaeyqa6VUQ9MOix5QV5+n3nJiJEZ+GkuxxMax9JqUbal+gNEjDtrp4XaMkN3+f1INLB1pZCQhu811LkYz2Ffikk9t811GGtURAgFlfbuLFGEmQzy00jrYISGl4vLY/Qroq7PdkKWshAUoOwrNj4IcnInc+OxqWvah3mM1Jyqs/7DDfv9h8f73i8BY6FMwzDvlf/DSUxlX0vEaJR0OZs3ArC9YVMMHxXdS+YEVtQCebssZUe14NUqAEVb8CbyDIvqIy3tTJhtdjjWzDeO/9vOg/nZ3BLApkE/4XGMp53nv4LKjfDNKIcn+Qu+eu4a2lOw3HrNpt3uxqpcF+rRaibitaWyaveJES+PTPcECv6qyED2+Bwvjze2xODFtj9DDZYtBUql6yWamzZ2jB85fBju8NdtY+VvNQPl69l3vmreUm1zsMdkbfhw4BW4PZdCt/juUDH63+vKp9hpOcUm+ARz/fGHugip0yh7v907m7hubw2BcbOb9vnu6qItnj5JhJIpNRyROt0FALFJfz5BAoDt8xWP2O8YCgXzGFjby97iZlU2WapnnYW1RmvN9CJni9QVoIq7UyppawGuU1++vNJFLGtc4PDce0cexnvGcFZ/Y4u9rzOjmePA2cXUeOG67u9CKq1BiVoddqKCdjRFVi2X4IxKhGfKTmmn3Z1C6xetdMslivql4QqmtlaPJyeqDVkLqbjwYrPhSvP8iKnUe50fk+CcK8Z9OM9od0Gw/Giy1Q6gijXgfXj2xnWKCxQ3YqlxhEgWj9JOqfl1lfhdqkoPB4xOuFBQIZKxEsveE4cRs6lw1qRf/W+hWiB+Y34eIqRiydlGR1ZEv2GGOTV7/pkFLzZZOsYmX56BDgckgmOb+JObZDbuyis1b41QuUxCrmbMRD84xE2jTVz45tlpHI29cPYUyX7Iofr9spmNgnlzeuHWxYlFGroZzoaM3vNh1k9MNfR2x7bukRvnMNNjlKQK9La3diNjVGotvJy1cNYuaYjuQ2SkIIJak13OMkMY4acvWBb7rew/qgtrOkUITJ2PtPyJzC6BQkjsLldDChrZMWjtjFJuk8vvqT4lfgQ4lFrF7wZvTMzWBwu0xGdczi3g/XsbagSHfcDae2M/VrtM5M4dmpAzh0rJxDJV5y0hMrcliM0EZ5qalr/eRYuZ/fvLqcUm90rsntxRfzYepGGvt1kjbH3A2Z7epghjY1RZLHyU1jOnDTmA5IKRtU3okWnyOJ+YHBdHKo/IDTP4LWdW/q2n4o0mS+YP1++uc3jinErxzRAV4zz62jUSvIrZkQ/l+9hgIwtmsOVfFjn9cnl7vO6sKQ9k15YfoABraJbJSV4HLw+zM6csVg48KLajJTE+iYkxZTmICOhqJWgutYosxbsYfC4/o22j00Zdzxv3J8wI3KDzepMbQdBZPfguEz63aiNjVKQxYmYaIy5U/AAuiLdfs4/ZFIs9WzC7dy4VPfU1hq7hvp1rE9WzydTC0Y+wfdVRPTBGwNBYCs9AQcQhDQeRILlKqjqQkufIFgxCo8VZX7kZ2eyFvXDWHVrkJW7DpKssfJqE7ZNK6lnh8HNW2Of9lbqR0t33GE1bsL6W6hz0lNsGn/MdP9ewMZrO95G73Hn1gzgY2NVVbsPMqbS3ZyhtZb4a5+2+x4OFrq5bev/1RRBUPNqt2F3PfhWv55YS/D48t8Af52fBJPOx7AKaKfb4dlKi8X9uTWGpqvraEAa/YUGWbRS+Dvk3qw4u4zopzn6To9RHrkZXD54NZM6ptXK8JESsmDH//CFXN+jNh+RLVSKSgsY+KT3/HNBut1x6qDlffZxKREi43NycQPWw5x4dPfs+nAsagGW88s2lOnc3l3+W5dU3KY902sAwAHj5Xzua8HN/huZmcw2vH+daAn24/EV0HdjF+9QBnSrgk/7zTvVPbFun1IKaNac6YmVD/MLl7eWrqTJxfE7mfvC0jueGcl/kDtx8qf26uFadRJn1aNaGVQqdjG5mRCqVe2ukIj0FYbfvCzzew3ycWpaWIlk3r9QfYcPW64v3GyB7dT8GlwACO9jzCx/K9sC+ZUHo+HZhk1V5LlVyFQOmSncFH/vKgeIiM6ZvHIRX1MOyoCFB73cfbjC9l1JPKL+3mnheiJGkRKyX++td4YZ09hGYs2H6rFGSm0bJLMzWM66u5L9jj5v3O61focbGxqgnUFxWzYV2nC1Woo/qDkw1UFdTYfK22yzcakJLg4u6fSyyiIg59kB/ZRGfqdLMos97u3QoP0oaQmuMhJTyAjyR3q5ZFPWqKbm8Z0ZMH6/fj8QQa0aUK3FoqPoVuLdNbs0Y/QAli9u0g3m/2hTzfQu2UjhneomRjuWJR4AzH9FVoOFMdILKwhbhrTgfymyTzzzRbW7CnC7RSc3jWH343uQOdm6XUyBxub6nK0NNL8E9VTnkjzcm1zXp9c/v2FcaWPYe0zyUk31zDuGNeZJdsOs+vIcdqJ3bQS+yr2jU5YT3LZSmBYjcy3QQqUNk1TWHxXdKGz3EZJuq1urx/Zjt++rt9POdHtMC2NcstbK1h81+g6iXhxOwUOYS0GPUxdmpom9M5lQu9cvP4gToc4KbP3bWzMaJOVgqAyUDKqwRZUqcNqlefTNIXfndaex77cFLUvPdHFX86Orf3npCfy/oxhfPjRB1yw5s8kU2myS/YfgRfPhvOegl4XV3u+vwqTVyzO6dWCu87qjFtTcK15RiLdmptHSu0vLudNk+KPNUmCy8lpnbMtj++Yk2qY2VybeFwOW5jY1EuaZyRxRrdKH4M2bDg7LYGxqv11wczTO/LvS3rTIxS1meh2MKlPLv+bMcy0c6uazBQPUw4+EiFMKpBB+HAmlBlbaazSIDWUqnDtiHZM7JPHR6sLOFLio2NOKqO75HDty0tjHvvaDzu4ZECrOpil8uP6btMhjvvMG1Y1Sfbw6MV9fhW5AjY2Ncn9E3uw7WAp6/cVR/WUf+qKfnXeHkIIUaH9B4MSIaqQA7R3JexbbbzfWwLr5lZvotgCJYKstASmDMmP2Da8fVMWrDcPv90SIxKjJunWIoNXrxnEPR+srYhOczmgXXYaLofA5RCc0iGLKUNakx3DtmpjYxNN09QE3r9xGHNX7KH8hx9B1fG4b6u61/jVOKqq+RfvszBmb9XOrcIWKDG4sH9LHvx4PV6T8NvGKXUbPty3VWP+N2MY2w+VcLTUR+vMZNNWvDY2NvGR6HZyUf+WQD7MO9GzqQEa58ce0yS6M2y82D6UGGQkublpTAfTMef11haQqxtaZ6bQq2UjW5jY2NQWAVVEV6zK2SczWR2htUkkV3JT6FT9ApG2QLHANae0ZUjbTN19HbJTufqUtnU8IxsbmzpB3c/HVc9NyOc+Dmktore7k+CCOeCu/vuzTV4W8LgcPD99ALMXbOb1H3ewv7ic9EQX5/fL46bRHSwVc7SxsamH+FVRUS79vkX1hsx2cP1CWDYH1n8MQZ+itQy8BprUzKJYnKhmTLVJ//795dKlsaOzqoKUknJ/kASXw46gsrFpyJQVwX+nwuYvldcJaXDTSkhuYn5cPUYIsUxK2b+qx9smrzgRQpDodtrCxMamIXN4K8weVilMAMqL4YmBsG/NiZvXSU69EShCiDOFEOuFEJuEEHec6PnY2Ng0YN69Fgp3RG8vOQBvTYVg7RddrY/UC4EihHACTwDjgK7ApUKIrid2VjY2Ng2SgpWw60fj/Yc2wrbYfdp/jdQLgQIMBDZJKbdIKb3AG8CEEzwnGxubhsjBDRbGGBds/DVTXwRKLqAumLUrtK0CIcS1QoilQoilBw7UTWMpGxubBkiyfopA5JiG65ivDvVFoOh5wCPC06SUz0gp+0sp+2dl1U05eRsbmwZI/nD9fI0wCWnQ8cy6m089or4IlF1AS9XrPKBue3Ha2Nj8OnC6YfzDxpnx4/4JnpS6nVM9ob4IlCVAByFEGyGEB7gEqH5pTBsbGxs9Op8F0+ZBu9NAOAABrYfD5e9A70tP9OxOWupFpryU0i+EuBH4BHACc6SUdjC4jY1N7dF6KFzxHgRCDfac9eJxeUKpN5+QlHI+MP9Ez8PGxuZXhi1ILFNfTF42NjY2Nic5tkCxsbGxsakRbIFiY2NjY1Mj2ALFxsbGxqZGsAWKjY2NjU2NYAsUGxsbG5sawRYoNjY2NjY1QoPs2CiEOACUqDalAcUWD7c6tjbOWZ+uH8/YE339eMae6OvHM/bXfv14xp7o68cz9kReP1NKmWbxfFE0yIwdKWWWEELdAzgL2GrxcKtja+Oc9en68Yw90dePZ+yJvn48Y3/t149n7Im+fjxjT+T1D1o8ly62ycvGxsbGpkawBYqNjY2NTY3QIE1eIZ5R/X0K8K3F46yOrY1z1qfrxzP2RF8/nrEn+vrxjP21Xz+esSf6+vGMPdHXrzIN0ilvY2NjY1P32CYvGxsbG5sawRYoNjY2NjY1QoPzoQgh3gQmorw3b+j/UMs1GxsbG5sYBFGenR6UZ6cXKATWSSlHmh3YEDWU14FpwC8ocdcCCAAHQv/7Q//L0P+Fof+9KB9kEDgcOld5aHx4bInqWEKvvZoxMnSc2jklQ/+CodcBzZz9oX0B1f4gsA04pjquHCgL/fOFzrkN+Dm0LXwtn+p9hrepkar/pcEY9X7te9EbY3Sc3ueg5ZjB/NR/BzTbtPOWRH+usQh/zurjgpoxUmd7+LvUzlN7LOi/31hoz13V8xhRpvrb6Lx+zRij7zkewp+R9rM0Gqt97Y8xpipzqmnMrm/2Oer9lqzcO1bmoj2PT/W6HDgOFAE7QvsLgL+qrnmTlDIbuDDWBRucQJFS/g9YhPKFZIQ2C2BP6G8vygcqgNLQ3z7ATeWPvQzlg3SE/i8Mjd9B5WcWDP3tRtGCwkJJoEh2rUYkiPwio6aO0t5Yqs7TEkhSncsDJFIpUEToX9PQsUHVNkmlYDTSzgSVN7d2jHq7VzVHvfdltE27L/y33sNZi/qG0ruRwucKqF47dcaZEX4/DioXA3r3RHhceE5+oj+L8Hm0c62KZqynUcnn2zgAAAagSURBVMc6j5kwC887/Pq4ZkxYGAdV24we+n7Vtnjfm/qcfiKvp523ek7hRZseNfF51yThe0/vNxs0+Fvvu3YQeQ/o3WdW5qK9nvY+cYb+bQJmh66XCHSn8t4/CCCl3B/zgg0xyksIkQ/MA1r9f3tnF2JVFcXx39+Z6WN0tAgqsIciKrNIzax8sAxCyogsCgwTSyt6sh6KHkL6JqIHAx8ysCzBhx7MioiswMwiJfw2QqLUSoJIdGwwnQ9XD/vs7p4z59yZO3PHGS/rB8M5d5/9dfY5e6291hr2IWw3EF/MJioPagyV1X4rQVhHOoBxBb87c/lqJRX4w0Wc6FEoNDG4yT/cpP0s61s3DeiWHSF6qF3hnimMxvf7TOYoQW42ERauJwmL6dfNbE21gg1noSSMJyiLNwgDcyC5lndjNFMRwCcJyiPVtOdkxzgho+usg8oK+RTlFkiR1o6r3GiCnkrSO0vKlNGenKcrkXT1bgV58n07WUObgyFtK1UmZVZKM8X9q+aiqqUfjbeaKievTMqsg5TuAeSpheEa7zLreqQou88il2w1l22R63MgpDKpWtt54v6HEwghgs+z328RZOAySVdWq6BRFUozcBHBmniQ8FAuy65FjQthkvVQcTEIODu7JsKE6qKyyo+T0rI2xlJxc/xG+QowvvCpK6M5abMlSd9GXz9zf7Qm57EPXfRVovHlTN127SV5SM6319CXauRdXkqO+Xcx9iV1bUVrpSmXb7D9iMcin/VQqEc9RTGDwbZZ5IKJ450K47zQyY9TWV0DJR3v9DgUiuoYjFzrIqzM++tTf/cfFUPRPIqyIi4i0/EuKtNJ7XFBqMyPdLz/Lckbid6TtNzNBNn0CMFi2QpMqVZJwykUSQKWE6yHicD1hME8RMXdFS2AFioB9Bj8jg88Cr00gNkO/ETQ3F30FkITqTyIv+m9yVr0AR9L6vkjazP1F3cDkwmKMFUMaXA9/zK3E3YQTQP+MRZwSZLWRSUWEhlDeFFinv2EsUotuB6CPzW2XyZ4iyZQmeWRX1H2UCzM0glxiErwvmxCx/hYtTzpSjBvMRX5nGuhzBLtj6I8Ra7RaoIsVdb5Z1F0b2VtRn6nEncsi1PkA7xllP1zyHCNd39KobvgPC4Q88ozT/69LLrelKsnFdTxrznJ1wP8Rd/nfRa9Xb4nGBj5fKL3ojOVI0Wx0m5gGrAku76cICenEuRfKQ0XQ5H0JXB79jMdtJH0H6eBNcdxnNFOakmJsABeYWZvVivUcArFcRzHGRkazuXlOI7jjAyuUBzHcZy64ArFcRzHqQuuUBzHcZy64ArFcRzHqQuuUBynCpJekPR0HeubJGmnpB2SLq9XvY4zGnCF4jinl3nAx2Y2zcx+GY4GJDXqnl3OKMcViuPkkPScpH2SvgKuytIek/SDpF2S1klqldQmab+klizPeEkHJLVImippi6TdktZLOl/SXOAp4FFJGyW9LOnJpN1XJS3Nzp/J2tst6cUkz0eStkn6UdLjSXqHpJckbQVmnp6RcpzeuEJxnARJ04H5hK0n7gNmZJc+NLMZZjaFsP3EEjP7B/gauCvLMx9YZ2ZdwBrgWTO7DtgDPG9mnwErgeVmdhvwDrAoa3dMVn6tpDnAFcCNhO0upku6JWtjsZlNB24Alkq6IEsfC+w1s5vM7Nu6D4zjDABXKI7Tm1nAejM7bmbHgE+y9GslbZa0B1gAXJOlryJsnkd2XC1pAnCemW3K0t8HokL4HzM7AByWNA2YA+wws8PZ+RxgB2FjzkkEBQNBiewCthC+lxPTe4B1Q715xxkK/q0Jx+lL0X5E7wHzzGyXpIeB2QBm9p2kSyXdCjSZ2d5MoQyUVYQvjF4MvJulCXjNzN5OM0qaTdinbqaZHZf0NZVPK5wws8HsTOs4dcMtFMfpzTfAvZLOldQG3J2ltwF/ZvGSBbkyawifnl4NYGbtwBFJs7LrC4FNFLMeuIPgWtuQpW0AFksaByBpoqQLCd+pOJIpk0mE7cUdZ9TgForjJJjZdkkfADuBg8Dm7NIywvcgDhJiIm1JsbXAKwSlElkErJTUCvxKxS2Wb69T0kbgaLQwzOwLSVcD34evMdABPET4bMITknYD+whuL8cZNfhuw44zRCTdD9xjZgsHUXYMIU7ygJn9XPfOOc5pxC0UxxkCklYAdwJzB1F2MvAp4Z8AXJk4ZzxuoTiO4zh1wYPyjuM4Tl1wheI4juPUBVcojuM4Tl1wheI4juPUBVcojuM4Tl34D+NKXtkSGI60AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11a78cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "df[\"date\"] = pd.to_datetime(df[\"dteday\"])\n",
    "df[\"dayofyear\"] = df[\"date\"].dt.dayofyear\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sns.pointplot(data = df[[\"dayofyear\", \"cnt\", \"yr\"]], \n",
    "              x = \"dayofyear\", y = \"cnt\", hue = \"yr\", ax = ax)\n",
    "ax.set(title = \"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每年开始和结束的数量较少，中间多，骑行量可能和月份或者季节有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3季节与骑行量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x140691d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13fa2320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(data = df[[\"season\", \"cnt\"]], x = \"season\", y = \"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从小提琴图中可以看出每个季节骑行量的分布确实有差异"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14376400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.barplot(data = df[[\"season\", \"cnt\"]], x = \"season\", y = \"cnt\")\n",
    "ax.set(title = \"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "明显可以看出第一季度的骑行量最少，第三季度最多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4月份与骑行量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x143e1160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.barplot(data = df[[\"mnth\", \"cnt\"]], x = \"mnth\", y = \"cnt\")\n",
    "ax.set(title = \"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "第一月份骑行量最少，5-10月份骑行量比较多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5天气与骑行量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Weathersit distribution of counts')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1438b160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "sns.barplot(data = df[[\"weathersit\", \"cnt\"]], x = \"weathersit\", y = \"cnt\")\n",
    "ax.set(title = \"Weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，天气与骑行量关系明显，其中有一类天气没有骑行量（大雨，大雪，大雾天气）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.6工作日和节假日骑行量的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x154d4780>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1438b6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1, ax2) = plt.subplots(ncols =2)\n",
    "sns.barplot(data = df, x = \"holiday\", y = \"cnt\", ax =ax1)\n",
    "sns.barplot(data = df, x = \"workingday\", y = \"cnt\", ax = ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从图中可以看出，工作日与节假日对骑行量的影响不大，特别是否为工作日，几乎没影响"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.7数值型特征和骑行量之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x5ad16a0>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x15571898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = df[[\"temp\", \"atemp\", \"hum\", \"windspeed\", \n",
    "               \"casual\", \"registered\",\"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sns.heatmap(corrMatt, mask = mask, vmax= .8, square = True, annot = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度相关度很高"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
